Overview

Dataset statistics

Number of variables60
Number of observations619
Missing cells5496
Missing cells (%)14.8%
Duplicate rows39
Duplicate rows (%)6.3%
Total size in memory847.7 KiB
Average record size in memory1.4 KiB

Variable types

NUM27
BOOL18
CAT14
URL1

Warnings

winner_Bundesliga has constant value "619" Constant
winner_C3 has constant value "619" Constant
finalist_C3 has constant value "619" Constant
winner_UCL has constant value "619" Constant
finalist_UCL has constant value "619" Constant
winner_Club WC has constant value "619" Constant
finalist_Club WC has constant value "619" Constant
winner_Liga has constant value "619" Constant
winner_Ligue 1 has constant value "619" Constant
winner_PL has constant value "619" Constant
winner_Serie A has constant value "619" Constant
finalist_WC has constant value "619" Constant
Dataset has 39 (6.3%) duplicate rows Duplicates
Squad has a high cardinality: 51 distinct values High cardinality
Player has a high cardinality: 221 distinct values High cardinality
BO_JK has a high cardinality: 569 distinct values High cardinality
Nom has a high cardinality: 221 distinct values High cardinality
Club has a high cardinality: 138 distinct values High cardinality
% has a high cardinality: 317 distinct values High cardinality
UCL_JK has a high cardinality: 304 distinct values High cardinality
Squad_JK has a high cardinality: 304 distinct values High cardinality
Nation_JK has a high cardinality: 282 distinct values High cardinality
Starts_Playing is highly correlated with MP_Playing and 2 other fieldsHigh correlation
MP_Playing is highly correlated with Starts_Playing and 2 other fieldsHigh correlation
Min_Playing is highly correlated with MP_Playing and 2 other fieldsHigh correlation
Mins_Per_90_Playing is highly correlated with MP_Playing and 2 other fieldsHigh correlation
G_minus_PK is highly correlated with Gls and 7 other fieldsHigh correlation
Gls is highly correlated with G_minus_PK and 7 other fieldsHigh correlation
PKatt is highly correlated with PKHigh correlation
PK is highly correlated with PKattHigh correlation
Gls_Per is highly correlated with Gls and 11 other fieldsHigh correlation
Ast_Per is highly correlated with AstHigh correlation
Ast is highly correlated with Ast_PerHigh correlation
G+A_Per is highly correlated with Gls_Per and 7 other fieldsHigh correlation
G_minus_PK_Per is highly correlated with Gls and 11 other fieldsHigh correlation
G+A_minus_PK_Per is highly correlated with Gls_Per and 7 other fieldsHigh correlation
xG_Expected is highly correlated with Gls and 8 other fieldsHigh correlation
npxG_Expected is highly correlated with Gls and 8 other fieldsHigh correlation
npxG+xA_Expected is highly correlated with Gls and 11 other fieldsHigh correlation
xG_Per is highly correlated with Gls and 11 other fieldsHigh correlation
xA_Per is highly correlated with xA_ExpectedHigh correlation
xA_Expected is highly correlated with xA_PerHigh correlation
xG+xA_Per is highly correlated with Gls_Per and 9 other fieldsHigh correlation
npxG_Per is highly correlated with Gls and 11 other fieldsHigh correlation
npxG+xA_Per is highly correlated with Gls_Per and 7 other fieldsHigh correlation
Pays is highly correlated with NationHigh correlation
Nation is highly correlated with PaysHigh correlation
Ast has 80 (12.9%) missing values Missing
PKatt has 78 (12.6%) missing values Missing
Ast_Per has 80 (12.9%) missing values Missing
G+A_Per has 80 (12.9%) missing values Missing
G+A_minus_PK_Per has 80 (12.9%) missing values Missing
xG_Expected has 565 (91.3%) missing values Missing
npxG_Expected has 565 (91.3%) missing values Missing
xA_Expected has 565 (91.3%) missing values Missing
npxG+xA_Expected has 565 (91.3%) missing values Missing
xG_Per has 565 (91.3%) missing values Missing
xA_Per has 565 (91.3%) missing values Missing
xG+xA_Per has 565 (91.3%) missing values Missing
npxG_Per has 565 (91.3%) missing values Missing
npxG+xA_Per has 565 (91.3%) missing values Missing
% has 13 (2.1%) missing values Missing
BO_JK is uniformly distributed Uniform
Gls has 88 (14.2%) zeros Zeros
Ast has 83 (13.4%) zeros Zeros
G_minus_PK has 89 (14.4%) zeros Zeros
PK has 371 (59.9%) zeros Zeros
PKatt has 296 (47.8%) zeros Zeros
CrdY has 64 (10.3%) zeros Zeros
Gls_Per has 88 (14.2%) zeros Zeros
Ast_Per has 83 (13.4%) zeros Zeros
G+A_Per has 61 (9.9%) zeros Zeros
G_minus_PK_Per has 89 (14.4%) zeros Zeros
G+A_minus_PK_Per has 61 (9.9%) zeros Zeros
xG_Expected has 7 (1.1%) zeros Zeros
npxG_Expected has 7 (1.1%) zeros Zeros
xA_Expected has 8 (1.3%) zeros Zeros
npxG+xA_Expected has 7 (1.1%) zeros Zeros
xG_Per has 7 (1.1%) zeros Zeros
xA_Per has 9 (1.5%) zeros Zeros
xG+xA_Per has 7 (1.1%) zeros Zeros
npxG_Per has 7 (1.1%) zeros Zeros
npxG+xA_Per has 7 (1.1%) zeros Zeros

Reproduction

Analysis started2021-11-29 17:40:18.658991
Analysis finished2021-11-29 17:41:48.845811
Duration1 minute and 30.19 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Season_End_Year
Real number (ℝ≥0)

Distinct24
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.759289
Minimum1996
Maximum2019
Zeros0
Zeros (%)0.0%
Memory size5.0 KiB
2021-11-29T18:41:48.887614image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1996
5-th percentile1997
Q12001
median2008
Q32013
95-th percentile2018
Maximum2019
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.033798903
Coefficient of variation (CV)0.003503307862
Kurtosis-1.242561137
Mean2007.759289
Median Absolute Deviation (MAD)6
Skewness-0.09308010898
Sum1242803
Variance49.474327
MonotocityIncreasing
2021-11-29T18:41:48.983649image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%) 
2013396.3%
 
2012396.3%
 
1997355.7%
 
2018294.7%
 
2017294.7%
 
2016284.5%
 
1999274.4%
 
2007274.4%
 
2009264.2%
 
1998264.2%
 
2019254.0%
 
2006254.0%
 
2000243.9%
 
2008233.7%
 
2001233.7%
 
2010223.6%
 
2011223.6%
 
2004223.6%
 
2014223.6%
 
2002223.6%
 
1996223.6%
 
2005213.4%
 
2015213.4%
 
2003203.2%
 
ValueCountFrequency (%) 
1996223.6%
 
1997355.7%
 
1998264.2%
 
1999274.4%
 
2000243.9%
 
2001233.7%
 
2002223.6%
 
2003203.2%
 
2004223.6%
 
2005213.4%
 
ValueCountFrequency (%) 
2019254.0%
 
2018294.7%
 
2017294.7%
 
2016284.5%
 
2015213.4%
 
2014223.6%
 
2013396.3%
 
2012396.3%
 
2011223.6%
 
2010223.6%
 

Squad
Categorical

HIGH CARDINALITY

Distinct51
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
Real Madrid
86 
Barcelona
79 
Bayern Munich
56 
Juventus
46 
Manchester Utd
39 
Other values (46)
313 
ValueCountFrequency (%) 
Real Madrid8613.9%
 
Barcelona7912.8%
 
Bayern Munich569.0%
 
Juventus467.4%
 
Manchester Utd396.3%
 
Chelsea325.2%
 
Liverpool284.5%
 
Milan274.4%
 
Arsenal264.2%
 
Manchester City213.4%
 
Atlético Madrid213.4%
 
Inter132.1%
 
Paris S-G121.9%
 
Dortmund111.8%
 
Valencia101.6%
 
Monaco101.6%
 
Tottenham101.6%
 
Fiorentina91.5%
 
Lazio81.3%
 
Roma81.3%
 
Parma71.1%
 
Napoli50.8%
 
Sevilla50.8%
 
Werder Bremen40.6%
 
Marseille40.6%
 
Other values (26)426.8%
 
2021-11-29T18:41:49.106039image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique14 ?
Unique (%)2.3%
2021-11-29T18:41:49.213000image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length9
Mean length9.625201939
Min length4

Overview of Unicode Properties

Unique unicode characters52
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a69311.6%
 
e62110.4%
 
r4587.7%
 
n4507.6%
 
l3566.0%
 
i3395.7%
 
t2824.7%
 
d2774.6%
 
M2664.5%
 
2534.2%
 
c2464.1%
 
o2454.1%
 
s1983.3%
 
u1763.0%
 
h1622.7%
 
B1462.5%
 
R951.6%
 
v841.4%
 
y811.4%
 
C601.0%
 
L490.8%
 
A480.8%
 
J460.8%
 
U430.7%
 
m410.7%
 
Other values (27)2434.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter480580.6%
 
Uppercase Letter88414.8%
 
Space Separator2534.2%
 
Dash Punctuation120.2%
 
Decimal Number40.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M26630.1%
 
B14616.5%
 
R9510.7%
 
C606.8%
 
L495.5%
 
A485.4%
 
J465.2%
 
U434.9%
 
S212.4%
 
P192.1%
 
I151.7%
 
V131.5%
 
D121.4%
 
G121.4%
 
F101.1%
 
T101.1%
 
W80.9%
 
N60.7%
 
E20.2%
 
H20.2%
 
K10.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a69314.4%
 
e62112.9%
 
r4589.5%
 
n4509.4%
 
l3567.4%
 
i3397.1%
 
t2825.9%
 
d2775.8%
 
c2465.1%
 
o2455.1%
 
s1984.1%
 
u1763.7%
 
h1623.4%
 
v841.7%
 
y811.7%
 
m410.9%
 
p340.7%
 
é210.4%
 
z100.2%
 
k80.2%
 
b60.1%
 
g50.1%
 
f40.1%
 
ñ40.1%
 
x2< 0.1%
 
Other values (2)2< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
253100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-12100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0250.0%
 
4250.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin568995.5%
 
Common2694.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a69312.2%
 
e62110.9%
 
r4588.1%
 
n4507.9%
 
l3566.3%
 
i3396.0%
 
t2825.0%
 
d2774.9%
 
M2664.7%
 
c2464.3%
 
o2454.3%
 
s1983.5%
 
u1763.1%
 
h1622.8%
 
B1462.6%
 
R951.7%
 
v841.5%
 
y811.4%
 
C601.1%
 
L490.9%
 
A480.8%
 
J460.8%
 
U430.8%
 
m410.7%
 
p340.6%
 
Other values (23)1933.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
25394.1%
 
-124.5%
 
020.7%
 
420.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII593299.6%
 
None260.4%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a69311.7%
 
e62110.5%
 
r4587.7%
 
n4507.6%
 
l3566.0%
 
i3395.7%
 
t2824.8%
 
d2774.7%
 
M2664.5%
 
2534.3%
 
c2464.1%
 
o2454.1%
 
s1983.3%
 
u1763.0%
 
h1622.7%
 
B1462.5%
 
R951.6%
 
v841.4%
 
y811.4%
 
C601.0%
 
L490.8%
 
A480.8%
 
J460.8%
 
U430.7%
 
m410.7%
 
Other values (24)2173.7%
 

Most frequent None characters

ValueCountFrequency (%) 
é2180.8%
 
ñ415.4%
 
ö13.8%
 

Comp
Categorical

Distinct6
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
La Liga
209 
Premier League
165 
Serie A
130 
Bundesliga
83 
Ligue 1
22 
ValueCountFrequency (%) 
La Liga20933.8%
 
Premier League16526.7%
 
Serie A13021.0%
 
Bundesliga8313.4%
 
Ligue 1223.6%
 
Division 1101.6%
 
2021-11-29T18:41:49.313936image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-29T18:41:49.382030image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:41:49.464608image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length7
Mean length9.316639742
Min length7

Overview of Unicode Properties

Unique unicode characters21
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e102517.8%
 
a66611.5%
 
i63911.1%
 
L60510.5%
 
5369.3%
 
g4798.3%
 
r4608.0%
 
u2704.7%
 
P1652.9%
 
m1652.9%
 
S1302.3%
 
A1302.3%
 
n931.6%
 
s931.6%
 
B831.4%
 
d831.4%
 
l831.4%
 
1320.6%
 
D100.2%
 
v100.2%
 
o100.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter407670.7%
 
Uppercase Letter112319.5%
 
Space Separator5369.3%
 
Decimal Number320.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
L60553.9%
 
P16514.7%
 
S13011.6%
 
A13011.6%
 
B837.4%
 
D100.9%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e102525.1%
 
a66616.3%
 
i63915.7%
 
g47911.8%
 
r46011.3%
 
u2706.6%
 
m1654.0%
 
n932.3%
 
s932.3%
 
d832.0%
 
l832.0%
 
v100.2%
 
o100.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
536100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
132100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin519990.2%
 
Common5689.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e102519.7%
 
a66612.8%
 
i63912.3%
 
L60511.6%
 
g4799.2%
 
r4608.8%
 
u2705.2%
 
P1653.2%
 
m1653.2%
 
S1302.5%
 
A1302.5%
 
n931.8%
 
s931.8%
 
B831.6%
 
d831.6%
 
l831.6%
 
D100.2%
 
v100.2%
 
o100.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
53694.4%
 
1325.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5767100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e102517.8%
 
a66611.5%
 
i63911.1%
 
L60510.5%
 
5369.3%
 
g4798.3%
 
r4608.0%
 
u2704.7%
 
P1652.9%
 
m1652.9%
 
S1302.3%
 
A1302.3%
 
n931.6%
 
s931.6%
 
B831.4%
 
d831.4%
 
l831.4%
 
1320.6%
 
D100.2%
 
v100.2%
 
o100.2%
 

Player
Categorical

HIGH CARDINALITY

Distinct221
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
Cristiano Ronaldo
 
16
Lionel Messi
 
14
Zinédine Zidane
 
11
Thierry Henry
 
9
Gianluigi Buffon
 
9
Other values (216)
560 
ValueCountFrequency (%) 
Cristiano Ronaldo162.6%
 
Lionel Messi142.3%
 
Zinédine Zidane111.8%
 
Thierry Henry91.5%
 
Gianluigi Buffon91.5%
 
Didier Drogba91.5%
 
Andrés Iniesta91.5%
 
Karim Benzema81.3%
 
Xavi81.3%
 
Sergio Agüero81.3%
 
Samuel Eto'o81.3%
 
Sergio Ramos71.1%
 
Manuel Neuer71.1%
 
Yaya Touré71.1%
 
Luis Suárez71.1%
 
Luís Figo71.1%
 
Eden Hazard71.1%
 
Thomas Müller71.1%
 
Kaká71.1%
 
Wayne Rooney71.1%
 
Iker Casillas71.1%
 
David Beckham61.0%
 
Rivaldo61.0%
 
Alessandro Del Piero61.0%
 
Andrea Pirlo61.0%
 
Other values (196)41667.2%
 
2021-11-29T18:41:49.582304image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique91 ?
Unique (%)14.7%
2021-11-29T18:41:49.692394image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length13
Mean length12.75767367
Min length3

Overview of Unicode Properties

Unique unicode characters69
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a7679.7%
 
e6778.6%
 
i6217.9%
 
5897.5%
 
r5306.7%
 
n5296.7%
 
o5216.6%
 
l3584.5%
 
s3003.8%
 
d2383.0%
 
u1902.4%
 
t1882.4%
 
m1241.6%
 
h1221.5%
 
g1201.5%
 
R1201.5%
 
y1021.3%
 
c1001.3%
 
b971.2%
 
M951.2%
 
D901.1%
 
v901.1%
 
S801.0%
 
B771.0%
 
A761.0%
 
Other values (44)109613.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter609277.1%
 
Uppercase Letter120015.2%
 
Space Separator5897.5%
 
Other Punctuation100.1%
 
Dash Punctuation60.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R12010.0%
 
M957.9%
 
D907.5%
 
S806.7%
 
B776.4%
 
A766.3%
 
C665.5%
 
L665.5%
 
P635.2%
 
G605.0%
 
F564.7%
 
K534.4%
 
T514.2%
 
E383.2%
 
H292.4%
 
N272.2%
 
J262.2%
 
Z262.2%
 
I221.8%
 
V211.8%
 
O131.1%
 
X121.0%
 
Y110.9%
 
W110.9%
 
Ö60.5%
 
Other values (3)50.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a76712.6%
 
e67711.1%
 
i62110.2%
 
r5308.7%
 
n5298.7%
 
o5218.6%
 
l3585.9%
 
s3004.9%
 
d2383.9%
 
u1903.1%
 
t1883.1%
 
m1242.0%
 
h1222.0%
 
g1202.0%
 
y1021.7%
 
c1001.6%
 
b971.6%
 
v901.5%
 
k721.2%
 
z591.0%
 
é550.9%
 
p480.8%
 
f420.7%
 
w260.4%
 
á260.4%
 
Other values (13)901.5%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
589100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
'10100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-6100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin729292.3%
 
Common6057.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a76710.5%
 
e6779.3%
 
i6218.5%
 
r5307.3%
 
n5297.3%
 
o5217.1%
 
l3584.9%
 
s3004.1%
 
d2383.3%
 
u1902.6%
 
t1882.6%
 
m1241.7%
 
h1221.7%
 
g1201.6%
 
R1201.6%
 
y1021.4%
 
c1001.4%
 
b971.3%
 
M951.3%
 
D901.2%
 
v901.2%
 
S801.1%
 
B771.1%
 
A761.0%
 
k721.0%
 
Other values (41)100813.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
58997.4%
 
'101.7%
 
-61.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII774398.0%
 
None1542.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a7679.9%
 
e6778.7%
 
i6218.0%
 
5897.6%
 
r5306.8%
 
n5296.8%
 
o5216.7%
 
l3584.6%
 
s3003.9%
 
d2383.1%
 
u1902.5%
 
t1882.4%
 
m1241.6%
 
h1221.6%
 
g1201.5%
 
R1201.5%
 
y1021.3%
 
c1001.3%
 
b971.3%
 
M951.2%
 
D901.2%
 
v901.2%
 
S801.0%
 
B771.0%
 
A761.0%
 
Other values (28)94212.2%
 

Most frequent None characters

ValueCountFrequency (%) 
é5535.7%
 
á2616.9%
 
ü1811.7%
 
í159.7%
 
ú106.5%
 
ó63.9%
 
Ö63.9%
 
à42.6%
 
ö31.9%
 
Š21.3%
 
ä21.3%
 
É21.3%
 
ž21.3%
 
š10.6%
 
Á10.6%
 
ë10.6%
 

Nation
Categorical

HIGH CORRELATION

Distinct39
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
FRA
79 
ESP
76 
GER
65 
BRA
63 
ITA
58 
Other values (34)
278 
ValueCountFrequency (%) 
FRA7912.8%
 
ESP7612.3%
 
GER6510.5%
 
BRA6310.2%
 
ITA589.4%
 
ARG447.1%
 
ENG416.6%
 
NED345.5%
 
POR304.8%
 
BEL162.6%
 
CIV162.6%
 
URU162.6%
 
WAL101.6%
 
CMR81.3%
 
COL71.1%
 
POL61.0%
 
CZE50.8%
 
SEN50.8%
 
CHI40.6%
 
DEN30.5%
 
GHA30.5%
 
GAB30.5%
 
NGA30.5%
 
GRE20.3%
 
BUL20.3%
 
Other values (14)203.2%
 
2021-11-29T18:41:49.809510image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique8 ?
Unique (%)1.3%
2021-11-29T18:41:49.910596image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters23
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
R31617.0%
 
A26514.3%
 
E24913.4%
 
G1668.9%
 
P1126.0%
 
N894.8%
 
B884.7%
 
I854.6%
 
S844.5%
 
F804.3%
 
T603.2%
 
O482.6%
 
L472.5%
 
C422.3%
 
D372.0%
 
U351.9%
 
V181.0%
 
W100.5%
 
H90.5%
 
M90.5%
 
Z50.3%
 
Y20.1%
 
K10.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1857100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R31617.0%
 
A26514.3%
 
E24913.4%
 
G1668.9%
 
P1126.0%
 
N894.8%
 
B884.7%
 
I854.6%
 
S844.5%
 
F804.3%
 
T603.2%
 
O482.6%
 
L472.5%
 
C422.3%
 
D372.0%
 
U351.9%
 
V181.0%
 
W100.5%
 
H90.5%
 
M90.5%
 
Z50.3%
 
Y20.1%
 
K10.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1857100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
R31617.0%
 
A26514.3%
 
E24913.4%
 
G1668.9%
 
P1126.0%
 
N894.8%
 
B884.7%
 
I854.6%
 
S844.5%
 
F804.3%
 
T603.2%
 
O482.6%
 
L472.5%
 
C422.3%
 
D372.0%
 
U351.9%
 
V181.0%
 
W100.5%
 
H90.5%
 
M90.5%
 
Z50.3%
 
Y20.1%
 
K10.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1857100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
R31617.0%
 
A26514.3%
 
E24913.4%
 
G1668.9%
 
P1126.0%
 
N894.8%
 
B884.7%
 
I854.6%
 
S844.5%
 
F804.3%
 
T603.2%
 
O482.6%
 
L472.5%
 
C422.3%
 
D372.0%
 
U351.9%
 
V181.0%
 
W100.5%
 
H90.5%
 
M90.5%
 
Z50.3%
 
Y20.1%
 
K10.1%
 

Pos
Categorical

Distinct7
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
FW
182 
FW,MF
144 
MF
139 
DF
61 
GK
52 
Other values (2)
41 
ValueCountFrequency (%) 
FW18229.4%
 
FW,MF14423.3%
 
MF13922.5%
 
DF619.9%
 
GK528.4%
 
DF,MF365.8%
 
MF,FW50.8%
 
2021-11-29T18:41:49.998409image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-29T18:41:50.066366image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:41:50.145203image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length2
Mean length2.896607431
Min length2

Overview of Unicode Properties

Unique unicode characters7
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
F75241.9%
 
W33118.5%
 
M32418.1%
 
,18510.3%
 
D975.4%
 
G522.9%
 
K522.9%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter160889.7%
 
Other Punctuation18510.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
F75246.8%
 
W33120.6%
 
M32420.1%
 
D976.0%
 
G523.2%
 
K523.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,185100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin160889.7%
 
Common18510.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
F75246.8%
 
W33120.6%
 
M32420.1%
 
D976.0%
 
G523.2%
 
K523.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
,185100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1793100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
F75241.9%
 
W33118.5%
 
M32418.1%
 
,18510.3%
 
D975.4%
 
G522.9%
 
K522.9%
 

Age
Real number (ℝ≥0)

Distinct22
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.98707593
Minimum17
Maximum38
Zeros0
Zeros (%)0.0%
Memory size5.0 KiB
2021-11-29T18:41:50.227739image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile20
Q123
median26
Q328
95-th percentile32
Maximum38
Range21
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.561275136
Coefficient of variation (CV)0.1370402405
Kurtosis0.1140199329
Mean25.98707593
Median Absolute Deviation (MAD)2
Skewness0.2092496384
Sum16086
Variance12.68268059
MonotocityNot monotonic
2021-11-29T18:41:50.310428image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
257111.5%
 
277011.3%
 
266310.2%
 
23609.7%
 
28579.2%
 
24538.6%
 
29447.1%
 
30416.6%
 
22345.5%
 
21315.0%
 
31274.4%
 
20172.7%
 
32121.9%
 
33111.8%
 
1991.5%
 
1850.8%
 
1730.5%
 
3430.5%
 
3720.3%
 
3520.3%
 
3620.3%
 
3820.3%
 
ValueCountFrequency (%) 
1730.5%
 
1850.8%
 
1991.5%
 
20172.7%
 
21315.0%
 
22345.5%
 
23609.7%
 
24538.6%
 
257111.5%
 
266310.2%
 
ValueCountFrequency (%) 
3820.3%
 
3720.3%
 
3620.3%
 
3520.3%
 
3430.5%
 
33111.8%
 
32121.9%
 
31274.4%
 
30416.6%
 
29447.1%
 

Born
Real number (ℝ≥0)

Distinct35
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1980.458805
Minimum1961
Maximum1998
Zeros0
Zeros (%)0.0%
Memory size5.0 KiB
2021-11-29T18:41:50.406326image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1961
5-th percentile1968
Q11975
median1981
Q31987
95-th percentile1992
Maximum1998
Range37
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.660230116
Coefficient of variation (CV)0.00386790682
Kurtosis-0.8118916011
Mean1980.458805
Median Absolute Deviation (MAD)6
Skewness-0.1611452585
Sum1225904
Variance58.67912543
MonotocityNot monotonic
2021-11-29T18:41:50.504369image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%) 
1987487.8%
 
1978355.7%
 
1976355.7%
 
1972335.3%
 
1973304.8%
 
1984304.8%
 
1981284.5%
 
1988284.5%
 
1986284.5%
 
1991274.4%
 
1983264.2%
 
1985264.2%
 
1968254.0%
 
1980243.9%
 
1992213.4%
 
1977213.4%
 
1975162.6%
 
1989162.6%
 
1979162.6%
 
1969142.3%
 
1982132.1%
 
1974121.9%
 
1993111.8%
 
197091.5%
 
197181.3%
 
Other values (10)396.3%
 
ValueCountFrequency (%) 
196110.2%
 
196210.2%
 
196330.5%
 
196420.3%
 
196550.8%
 
196650.8%
 
196761.0%
 
1968254.0%
 
1969142.3%
 
197091.5%
 
ValueCountFrequency (%) 
199850.8%
 
199430.5%
 
1993111.8%
 
1992213.4%
 
1991274.4%
 
199081.3%
 
1989162.6%
 
1988284.5%
 
1987487.8%
 
1986284.5%
 

MP_Playing
Real number (ℝ≥0)

HIGH CORRELATION

Distinct40
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.42326333
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Memory size5.0 KiB
2021-11-29T18:41:50.614375image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.9
Q128
median32
Q334
95-th percentile37
Maximum42
Range41
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.134826179
Coefficient of variation (CV)0.2016491825
Kurtosis4.686223272
Mean30.42326333
Median Absolute Deviation (MAD)3
Skewness-1.803655791
Sum18832
Variance37.63609225
MonotocityNot monotonic
2021-11-29T18:41:50.714660image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%) 
326610.7%
 
346310.2%
 
35589.4%
 
31508.1%
 
33436.9%
 
29416.6%
 
30406.5%
 
36365.8%
 
37325.2%
 
28274.4%
 
27254.0%
 
38233.7%
 
26193.1%
 
24152.4%
 
25121.9%
 
22101.6%
 
2391.5%
 
2071.1%
 
1650.8%
 
1950.8%
 
1840.6%
 
2130.5%
 
1330.5%
 
1730.5%
 
130.5%
 
Other values (15)172.7%
 
ValueCountFrequency (%) 
130.5%
 
210.2%
 
410.2%
 
510.2%
 
710.2%
 
810.2%
 
910.2%
 
1020.3%
 
1110.2%
 
1220.3%
 
ValueCountFrequency (%) 
4210.2%
 
4110.2%
 
4010.2%
 
3910.2%
 
38233.7%
 
37325.2%
 
36365.8%
 
35589.4%
 
346310.2%
 
33436.9%
 

Starts_Playing
Real number (ℝ≥0)

HIGH CORRELATION

Distinct39
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.30371567
Minimum0
Maximum41
Zeros3
Zeros (%)0.5%
Memory size5.0 KiB
2021-11-29T18:41:50.820855image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q125
median30
Q333
95-th percentile37
Maximum41
Range41
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.16189366
Coefficient of variation (CV)0.2530372246
Kurtosis2.210856266
Mean28.30371567
Median Absolute Deviation (MAD)4
Skewness-1.347425143
Sum17520
Variance51.2927208
MonotocityNot monotonic
2021-11-29T18:41:50.919485image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%) 
31599.5%
 
32528.4%
 
35426.8%
 
30406.5%
 
34396.3%
 
33386.1%
 
29355.7%
 
28325.2%
 
26274.4%
 
37254.0%
 
24243.9%
 
27243.9%
 
25233.7%
 
36213.4%
 
23172.7%
 
22162.6%
 
20152.4%
 
38132.1%
 
2181.3%
 
1871.1%
 
1171.1%
 
1771.1%
 
1561.0%
 
1961.0%
 
1661.0%
 
Other values (14)304.8%
 
ValueCountFrequency (%) 
030.5%
 
120.3%
 
220.3%
 
510.2%
 
620.3%
 
730.5%
 
820.3%
 
920.3%
 
1171.1%
 
1220.3%
 
ValueCountFrequency (%) 
4120.3%
 
4010.2%
 
3910.2%
 
38132.1%
 
37254.0%
 
36213.4%
 
35426.8%
 
34396.3%
 
33386.1%
 
32528.4%
 

Min_Playing
Real number (ℝ≥0)

HIGH CORRELATION

Distinct469
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2491.943457
Minimum6
Maximum3622
Zeros0
Zeros (%)0.0%
Memory size5.0 KiB
2021-11-29T18:41:51.027542image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile1319.6
Q12160.5
median2624
Q32930.5
95-th percentile3305.6
Maximum3622
Range3616
Interquartile range (IQR)770

Descriptive statistics

Standard deviation629.1371255
Coefficient of variation (CV)0.2524684594
Kurtosis1.941896288
Mean2491.943457
Median Absolute Deviation (MAD)357
Skewness-1.225330936
Sum1542513
Variance395813.5227
MonotocityNot monotonic
2021-11-29T18:41:51.139696image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3150121.9%
 
342081.3%
 
333081.3%
 
324061.0%
 
279050.8%
 
234140.6%
 
209140.6%
 
288040.6%
 
297040.6%
 
285740.6%
 
306040.6%
 
250130.5%
 
283030.5%
 
272230.5%
 
258130.5%
 
196330.5%
 
243430.5%
 
313830.5%
 
308630.5%
 
293330.5%
 
199730.5%
 
215930.5%
 
255030.5%
 
295330.5%
 
290730.5%
 
Other values (444)51483.0%
 
ValueCountFrequency (%) 
610.2%
 
4510.2%
 
5310.2%
 
7410.2%
 
12210.2%
 
16410.2%
 
20510.2%
 
54210.2%
 
56010.2%
 
60610.2%
 
ValueCountFrequency (%) 
362210.2%
 
352410.2%
 
352010.2%
 
347310.2%
 
342081.3%
 
341310.2%
 
338410.2%
 
337810.2%
 
337510.2%
 
336710.2%
 

Mins_Per_90_Playing
Real number (ℝ≥0)

HIGH CORRELATION

Distinct217
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.68546042
Minimum0.1
Maximum40.2
Zeros0
Zeros (%)0.0%
Memory size5.0 KiB
2021-11-29T18:41:51.261307image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile14.69
Q124
median29.2
Q332.6
95-th percentile36.71
Maximum40.2
Range40.1
Interquartile range (IQR)8.6

Descriptive statistics

Standard deviation6.990401318
Coefficient of variation (CV)0.2524935909
Kurtosis1.938683353
Mean27.68546042
Median Absolute Deviation (MAD)3.9
Skewness-1.224693573
Sum17137.3
Variance48.86571059
MonotocityNot monotonic
2021-11-29T18:41:51.379455image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
35121.9%
 
37101.6%
 
33.991.5%
 
3191.5%
 
29.491.5%
 
3281.3%
 
3881.3%
 
30.271.1%
 
29.271.1%
 
2871.1%
 
31.771.1%
 
3071.1%
 
28.871.1%
 
28.661.0%
 
3661.0%
 
21.161.0%
 
16.461.0%
 
32.861.0%
 
32.361.0%
 
28.461.0%
 
2661.0%
 
31.461.0%
 
32.661.0%
 
29.661.0%
 
2450.8%
 
Other values (192)44171.2%
 
ValueCountFrequency (%) 
0.110.2%
 
0.510.2%
 
0.610.2%
 
0.810.2%
 
1.410.2%
 
1.810.2%
 
2.310.2%
 
610.2%
 
6.210.2%
 
6.710.2%
 
ValueCountFrequency (%) 
40.210.2%
 
39.210.2%
 
39.110.2%
 
38.610.2%
 
3881.3%
 
37.910.2%
 
37.610.2%
 
37.520.3%
 
37.410.2%
 
37.210.2%
 

Gls
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct43
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.88206785
Minimum0
Maximum50
Zeros88
Zeros (%)14.2%
Memory size5.0 KiB
2021-11-29T18:41:51.504440image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median8
Q317
95-th percentile30
Maximum50
Range50
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.00100245
Coefficient of variation (CV)0.9190351127
Kurtosis0.4525178374
Mean10.88206785
Median Absolute Deviation (MAD)7
Skewness0.9620903597
Sum6736
Variance100.0200501
MonotocityNot monotonic
2021-11-29T18:41:51.614441image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%) 
08814.2%
 
1365.8%
 
3355.7%
 
4315.0%
 
2304.8%
 
13254.0%
 
6254.0%
 
7254.0%
 
5254.0%
 
16243.9%
 
9223.6%
 
10213.4%
 
12203.2%
 
21193.1%
 
8182.9%
 
17142.3%
 
24142.3%
 
15132.1%
 
25111.8%
 
11111.8%
 
14101.6%
 
19101.6%
 
26101.6%
 
2391.5%
 
2281.3%
 
Other values (18)6510.5%
 
ValueCountFrequency (%) 
08814.2%
 
1365.8%
 
2304.8%
 
3355.7%
 
4315.0%
 
5254.0%
 
6254.0%
 
7254.0%
 
8182.9%
 
9223.6%
 
ValueCountFrequency (%) 
5010.2%
 
4810.2%
 
4620.3%
 
4310.2%
 
4020.3%
 
3710.2%
 
3620.3%
 
3520.3%
 
3440.6%
 
3310.2%
 

Ast
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct21
Distinct (%)3.9%
Missing80
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean5.703153989
Minimum0
Maximum20
Zeros83
Zeros (%)13.4%
Memory size5.0 KiB
2021-11-29T18:41:51.712406image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile14
Maximum20
Range20
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.47971567
Coefficient of variation (CV)0.7854803989
Kurtosis-0.02494505676
Mean5.703153989
Median Absolute Deviation (MAD)3
Skewness0.650996592
Sum3074
Variance20.06785249
MonotocityNot monotonic
2021-11-29T18:41:51.803586image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
08313.4%
 
5569.0%
 
8426.8%
 
4416.6%
 
1406.5%
 
7376.0%
 
3376.0%
 
6355.7%
 
9335.3%
 
2315.0%
 
11284.5%
 
10213.4%
 
12142.3%
 
13111.8%
 
1481.3%
 
1661.0%
 
1540.6%
 
1740.6%
 
1840.6%
 
2020.3%
 
1920.3%
 
(Missing)8012.9%
 
ValueCountFrequency (%) 
08313.4%
 
1406.5%
 
2315.0%
 
3376.0%
 
4416.6%
 
5569.0%
 
6355.7%
 
7376.0%
 
8426.8%
 
9335.3%
 
ValueCountFrequency (%) 
2020.3%
 
1920.3%
 
1840.6%
 
1740.6%
 
1661.0%
 
1540.6%
 
1481.3%
 
13111.8%
 
12142.3%
 
11284.5%
 

G_minus_PK
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct39
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.605815832
Minimum0
Maximum42
Zeros89
Zeros (%)14.4%
Memory size5.0 KiB
2021-11-29T18:41:51.910889image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q315
95-th percentile27
Maximum42
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.736878193
Coefficient of variation (CV)0.9095404644
Kurtosis0.2685954515
Mean9.605815832
Median Absolute Deviation (MAD)6
Skewness0.9364130405
Sum5946
Variance76.33304056
MonotocityNot monotonic
2021-11-29T18:41:52.009457image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%) 
08914.4%
 
1376.0%
 
3365.8%
 
2345.5%
 
4335.3%
 
6304.8%
 
7304.8%
 
5284.5%
 
10284.5%
 
8264.2%
 
12243.9%
 
19162.6%
 
9162.6%
 
21162.6%
 
14162.6%
 
13152.4%
 
16152.4%
 
15142.3%
 
20132.1%
 
11121.9%
 
23121.9%
 
18121.9%
 
28121.9%
 
17101.6%
 
2291.5%
 
Other values (14)365.8%
 
ValueCountFrequency (%) 
08914.4%
 
1376.0%
 
2345.5%
 
3365.8%
 
4335.3%
 
5284.5%
 
6304.8%
 
7304.8%
 
8264.2%
 
9162.6%
 
ValueCountFrequency (%) 
4210.2%
 
4010.2%
 
3820.3%
 
3710.2%
 
3410.2%
 
3320.3%
 
3240.6%
 
3120.3%
 
3010.2%
 
2930.5%
 

PK
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.276252019
Minimum0
Maximum12
Zeros371
Zeros (%)59.9%
Memory size5.0 KiB
2021-11-29T18:41:52.094662image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.020830401
Coefficient of variation (CV)1.58341015
Kurtosis3.219396489
Mean1.276252019
Median Absolute Deviation (MAD)0
Skewness1.805480801
Sum790
Variance4.083755509
MonotocityNot monotonic
2021-11-29T18:41:52.178379image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
037159.9%
 
1569.0%
 
2548.7%
 
3518.2%
 
4335.3%
 
5193.1%
 
6172.7%
 
791.5%
 
850.8%
 
1020.3%
 
910.2%
 
1210.2%
 
ValueCountFrequency (%) 
037159.9%
 
1569.0%
 
2548.7%
 
3518.2%
 
4335.3%
 
5193.1%
 
6172.7%
 
791.5%
 
850.8%
 
910.2%
 
ValueCountFrequency (%) 
1210.2%
 
1020.3%
 
910.2%
 
850.8%
 
791.5%
 
6172.7%
 
5193.1%
 
4335.3%
 
3518.2%
 
2548.7%
 

PKatt
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct14
Distinct (%)2.6%
Missing78
Missing (%)12.6%
Infinite0
Infinite (%)0.0%
Mean1.624768946
Minimum0
Maximum13
Zeros296
Zeros (%)47.8%
Memory size5.0 KiB
2021-11-29T18:41:52.259388image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile7
Maximum13
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.417079695
Coefficient of variation (CV)1.487645182
Kurtosis2.58612645
Mean1.624768946
Median Absolute Deviation (MAD)0
Skewness1.691519777
Sum879
Variance5.842274252
MonotocityNot monotonic
2021-11-29T18:41:52.345746image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%) 
029647.8%
 
1528.4%
 
2477.6%
 
3447.1%
 
4365.8%
 
6193.1%
 
8162.6%
 
5152.4%
 
781.3%
 
1030.5%
 
920.3%
 
1110.2%
 
1310.2%
 
1210.2%
 
(Missing)7812.6%
 
ValueCountFrequency (%) 
029647.8%
 
1528.4%
 
2477.6%
 
3447.1%
 
4365.8%
 
5152.4%
 
6193.1%
 
781.3%
 
8162.6%
 
920.3%
 
ValueCountFrequency (%) 
1310.2%
 
1210.2%
 
1110.2%
 
1030.5%
 
920.3%
 
8162.6%
 
781.3%
 
6193.1%
 
5152.4%
 
4365.8%
 

CrdY
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.012924071
Minimum0
Maximum16
Zeros64
Zeros (%)10.3%
Memory size5.0 KiB
2021-11-29T18:41:52.436957image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q36
95-th percentile10
Maximum16
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.036428399
Coefficient of variation (CV)0.7566623104
Kurtosis0.5870232341
Mean4.012924071
Median Absolute Deviation (MAD)2
Skewness0.8790238948
Sum2484
Variance9.219897423
MonotocityNot monotonic
2021-11-29T18:41:52.545347image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
29315.0%
 
38113.1%
 
47412.0%
 
17211.6%
 
56911.1%
 
06410.3%
 
6497.9%
 
8325.2%
 
7284.5%
 
9223.6%
 
10152.4%
 
1171.1%
 
1261.0%
 
1430.5%
 
1320.3%
 
1510.2%
 
1610.2%
 
ValueCountFrequency (%) 
06410.3%
 
17211.6%
 
29315.0%
 
38113.1%
 
47412.0%
 
56911.1%
 
6497.9%
 
7284.5%
 
8325.2%
 
9223.6%
 
ValueCountFrequency (%) 
1610.2%
 
1510.2%
 
1430.5%
 
1320.3%
 
1261.0%
 
1171.1%
 
10152.4%
 
9223.6%
 
8325.2%
 
7284.5%
 

CrdR
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
495 
1
95 
2
 
25
3
 
4
ValueCountFrequency (%) 
049580.0%
 
19515.3%
 
2254.0%
 
340.6%
 
2021-11-29T18:41:52.675057image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-11-29T18:41:52.743409image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:41:52.816405image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0111460.0%
 
.61933.3%
 
1955.1%
 
2251.3%
 
340.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number123866.7%
 
Other Punctuation61933.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0111490.0%
 
1957.7%
 
2252.0%
 
340.3%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.619100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1857100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0111460.0%
 
.61933.3%
 
1955.1%
 
2251.3%
 
340.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1857100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0111460.0%
 
.61933.3%
 
1955.1%
 
2251.3%
 
340.2%
 

Gls_Per
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct115
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39180937
Minimum0
Maximum1.56
Zeros88
Zeros (%)14.2%
Memory size5.0 KiB
2021-11-29T18:41:52.928346image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median0.33
Q30.64
95-th percentile0.981
Maximum1.56
Range1.56
Interquartile range (IQR)0.54

Descriptive statistics

Standard deviation0.3308764802
Coefficient of variation (CV)0.8444833268
Kurtosis-0.526125752
Mean0.39180937
Median Absolute Deviation (MAD)0.26
Skewness0.5998034457
Sum242.53
Variance0.1094792452
MonotocityNot monotonic
2021-11-29T18:41:53.050698image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
08814.2%
 
0.04142.3%
 
0.11142.3%
 
0.03132.1%
 
0.21132.1%
 
0.54111.8%
 
0.09111.8%
 
0.16111.8%
 
0.06101.6%
 
0.27101.6%
 
0.13101.6%
 
0.49101.6%
 
0.1491.5%
 
0.1991.5%
 
0.2691.5%
 
0.4481.3%
 
0.5981.3%
 
0.381.3%
 
0.6881.3%
 
0.0881.3%
 
0.6581.3%
 
0.5581.3%
 
0.181.3%
 
0.1871.1%
 
0.771.1%
 
Other values (90)29948.3%
 
ValueCountFrequency (%) 
08814.2%
 
0.03132.1%
 
0.04142.3%
 
0.0550.8%
 
0.06101.6%
 
0.0740.6%
 
0.0881.3%
 
0.09111.8%
 
0.181.3%
 
0.11142.3%
 
ValueCountFrequency (%) 
1.5610.2%
 
1.3910.2%
 
1.3810.2%
 
1.2710.2%
 
1.2420.3%
 
1.1910.2%
 
1.1810.2%
 
1.1510.2%
 
1.1410.2%
 
1.1310.2%
 

Ast_Per
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct59
Distinct (%)10.9%
Missing80
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean0.2099814471
Minimum0
Maximum0.65
Zeros83
Zeros (%)13.4%
Memory size5.0 KiB
2021-11-29T18:41:53.177519image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.07
median0.2
Q30.32
95-th percentile0.5
Maximum0.65
Range0.65
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.1596330586
Coefficient of variation (CV)0.7602245857
Kurtosis-0.46153109
Mean0.2099814471
Median Absolute Deviation (MAD)0.12
Skewness0.4878287241
Sum113.18
Variance0.02548271341
MonotocityNot monotonic
2021-11-29T18:41:53.305455image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
08313.4%
 
0.24203.2%
 
0.15203.2%
 
0.03172.7%
 
0.2162.6%
 
0.18152.4%
 
0.17152.4%
 
0.32142.3%
 
0.28132.1%
 
0.06132.1%
 
0.33132.1%
 
0.04121.9%
 
0.26121.9%
 
0.38121.9%
 
0.29111.8%
 
0.14101.6%
 
0.22101.6%
 
0.27101.6%
 
0.42101.6%
 
0.23101.6%
 
0.11101.6%
 
0.12101.6%
 
0.0791.5%
 
0.491.5%
 
0.1391.5%
 
Other values (34)15625.2%
 
(Missing)8012.9%
 
ValueCountFrequency (%) 
08313.4%
 
0.03172.7%
 
0.04121.9%
 
0.0540.6%
 
0.06132.1%
 
0.0791.5%
 
0.0861.0%
 
0.0991.5%
 
0.191.5%
 
0.11101.6%
 
ValueCountFrequency (%) 
0.6540.6%
 
0.6320.3%
 
0.620.3%
 
0.5910.2%
 
0.5810.2%
 
0.5730.5%
 
0.5520.3%
 
0.5420.3%
 
0.5340.6%
 
0.5250.8%
 

G+A_Per
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct137
Distinct (%)25.4%
Missing80
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean0.6125602968
Minimum0
Maximum1.94
Zeros61
Zeros (%)9.9%
Memory size5.0 KiB
2021-11-29T18:41:53.445674image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.235
median0.66
Q30.9
95-th percentile1.261
Maximum1.94
Range1.94
Interquartile range (IQR)0.665

Descriptive statistics

Standard deviation0.4169388952
Coefficient of variation (CV)0.6806495579
Kurtosis-0.5600665994
Mean0.6125602968
Median Absolute Deviation (MAD)0.3
Skewness0.1870830112
Sum330.17
Variance0.1738380424
MonotocityNot monotonic
2021-11-29T18:41:53.586596image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0619.9%
 
0.89121.9%
 
0.77101.6%
 
0.891.5%
 
0.6791.5%
 
0.1181.3%
 
0.1281.3%
 
0.0381.3%
 
0.7881.3%
 
0.0781.3%
 
0.4771.1%
 
0.6171.1%
 
0.8871.1%
 
0.471.1%
 
0.1771.1%
 
0.3871.1%
 
1.0371.1%
 
0.4261.0%
 
0.9561.0%
 
0.7461.0%
 
0.961.0%
 
0.8161.0%
 
161.0%
 
0.9761.0%
 
0.7661.0%
 
Other values (112)30148.6%
 
(Missing)8012.9%
 
ValueCountFrequency (%) 
0619.9%
 
0.0381.3%
 
0.0420.3%
 
0.0520.3%
 
0.0650.8%
 
0.0781.3%
 
0.0820.3%
 
0.0910.2%
 
0.120.3%
 
0.1181.3%
 
ValueCountFrequency (%) 
1.9410.2%
 
1.8610.2%
 
1.8210.2%
 
1.6320.3%
 
1.620.3%
 
1.5710.2%
 
1.5610.2%
 
1.5410.2%
 
1.5130.5%
 
1.4620.3%
 

G_minus_PK_Per
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct100
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3472051696
Minimum0
Maximum1.43
Zeros89
Zeros (%)14.4%
Memory size5.0 KiB
2021-11-29T18:41:53.730227image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.09
median0.28
Q30.56
95-th percentile0.871
Maximum1.43
Range1.43
Interquartile range (IQR)0.47

Descriptive statistics

Standard deviation0.29240401
Coefficient of variation (CV)0.8421649089
Kurtosis-0.475808095
Mean0.3472051696
Median Absolute Deviation (MAD)0.23
Skewness0.6267503317
Sum214.92
Variance0.08550010509
MonotocityNot monotonic
2021-11-29T18:41:53.865032image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
08914.4%
 
0.04152.4%
 
0.03142.3%
 
0.21132.1%
 
0.16132.1%
 
0.13121.9%
 
0.11121.9%
 
0.06121.9%
 
0.55121.9%
 
0.19111.8%
 
0.09101.6%
 
0.8101.6%
 
0.2691.5%
 
0.2491.5%
 
0.4291.5%
 
0.1491.5%
 
0.4391.5%
 
0.0881.3%
 
0.1881.3%
 
0.481.3%
 
0.2781.3%
 
0.6281.3%
 
0.5481.3%
 
0.181.3%
 
0.5671.1%
 
Other values (75)28846.5%
 
ValueCountFrequency (%) 
08914.4%
 
0.03142.3%
 
0.04152.4%
 
0.0550.8%
 
0.06121.9%
 
0.0750.8%
 
0.0881.3%
 
0.09101.6%
 
0.181.3%
 
0.11121.9%
 
ValueCountFrequency (%) 
1.4310.2%
 
1.2310.2%
 
1.120.3%
 
1.0630.5%
 
1.0120.3%
 
130.5%
 
0.9930.5%
 
0.9630.5%
 
0.9520.3%
 
0.9410.2%
 

G+A_minus_PK_Per
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct129
Distinct (%)23.9%
Missing80
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean0.566864564
Minimum0
Maximum1.8
Zeros61
Zeros (%)9.9%
Memory size5.0 KiB
2021-11-29T18:41:53.996410image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.235
median0.61
Q30.82
95-th percentile1.183
Maximum1.8
Range1.8
Interquartile range (IQR)0.585

Descriptive statistics

Standard deviation0.3807725494
Coefficient of variation (CV)0.6717169737
Kurtosis-0.5671558617
Mean0.566864564
Median Absolute Deviation (MAD)0.27
Skewness0.1727069787
Sum305.54
Variance0.1449877344
MonotocityNot monotonic
2021-11-29T18:41:54.121970image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0619.9%
 
0.77152.4%
 
0.67111.8%
 
0.76111.8%
 
0.38101.6%
 
0.1291.5%
 
0.7891.5%
 
0.1181.3%
 
0.6281.3%
 
0.0381.3%
 
0.7481.3%
 
0.6881.3%
 
0.0781.3%
 
0.7271.1%
 
0.1771.1%
 
0.6171.1%
 
0.471.1%
 
0.9271.1%
 
0.9771.1%
 
0.771.1%
 
0.7571.1%
 
0.871.1%
 
0.3361.0%
 
0.8961.0%
 
0.5761.0%
 
Other values (104)28445.9%
 
(Missing)8012.9%
 
ValueCountFrequency (%) 
0619.9%
 
0.0381.3%
 
0.0420.3%
 
0.0520.3%
 
0.0650.8%
 
0.0781.3%
 
0.0820.3%
 
0.0910.2%
 
0.120.3%
 
0.1181.3%
 
ValueCountFrequency (%) 
1.810.2%
 
1.5710.2%
 
1.5410.2%
 
1.5120.3%
 
1.510.2%
 
1.4920.3%
 
1.4610.2%
 
1.4510.2%
 
1.410.2%
 
1.3510.2%
 

Url
URL

Distinct225
Distinct (%)36.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
https://fbref.com/en/players/dea698d9/Cristiano-Ronaldo
 
16
https://fbref.com/en/players/d70ce98e/Lionel-Messi
 
14
https://fbref.com/en/players/654f4e63/Zinedine-Zidane
 
11
https://fbref.com/en/players/945dea33/Didier-Drogba
 
9
https://fbref.com/en/players/c0c5ee74/Thierry-Henry
 
9
Other values (220)
560 
ValueCountFrequency (%) 
https://fbref.com/en/players/dea698d9/Cristiano-Ronaldo162.6%
 
https://fbref.com/en/players/d70ce98e/Lionel-Messi142.3%
 
https://fbref.com/en/players/654f4e63/Zinedine-Zidane111.8%
 
https://fbref.com/en/players/945dea33/Didier-Drogba91.5%
 
https://fbref.com/en/players/c0c5ee74/Thierry-Henry91.5%
 
https://fbref.com/en/players/cfd65a29/Andres-Iniesta91.5%
 
https://fbref.com/en/players/e40d028b/Gianluigi-Buffon91.5%
 
https://fbref.com/en/players/70d74ece/Karim-Benzema81.3%
 
https://fbref.com/en/players/06420c93/Samuel-Etoo81.3%
 
https://fbref.com/en/players/4d034881/Sergio-Aguero81.3%
 
https://fbref.com/en/players/c1b4188c/Xavi81.3%
 
https://fbref.com/en/players/8778c910/Manuel-Neuer71.1%
 
https://fbref.com/en/players/1ddbb0da/Iker-Casillas71.1%
 
https://fbref.com/en/players/a39bb753/Eden-Hazard71.1%
 
https://fbref.com/en/players/f20f347f/Luis-Figo71.1%
 
https://fbref.com/en/players/f07be45a/Wayne-Rooney71.1%
 
https://fbref.com/en/players/08511d65/Sergio-Ramos71.1%
 
https://fbref.com/en/players/a6154613/Luis-Suarez71.1%
 
https://fbref.com/en/players/3c6089ab/Thomas-Muller71.1%
 
https://fbref.com/en/players/c204f844/Yaya-Toure71.1%
 
https://fbref.com/en/players/5c7f3153/Rivaldo61.0%
 
https://fbref.com/en/players/e46012d4/Kevin-De-Bruyne61.0%
 
https://fbref.com/en/players/80bc263c/Alessandro-Del-Piero61.0%
 
https://fbref.com/en/players/b2d6ea70/Kaka61.0%
 
https://fbref.com/en/players/71c5f16f/Andrea-Pirlo61.0%
 
Other values (200)41767.4%
 
ValueCountFrequency (%) 
https619100.0%
 
ValueCountFrequency (%) 
fbref.com619100.0%
 
ValueCountFrequency (%) 
/en/players/dea698d9/Cristiano-Ronaldo162.6%
 
/en/players/d70ce98e/Lionel-Messi142.3%
 
/en/players/654f4e63/Zinedine-Zidane111.8%
 
/en/players/945dea33/Didier-Drogba91.5%
 
/en/players/c0c5ee74/Thierry-Henry91.5%
 
/en/players/cfd65a29/Andres-Iniesta91.5%
 
/en/players/e40d028b/Gianluigi-Buffon91.5%
 
/en/players/70d74ece/Karim-Benzema81.3%
 
/en/players/06420c93/Samuel-Etoo81.3%
 
/en/players/4d034881/Sergio-Aguero81.3%
 
/en/players/c1b4188c/Xavi81.3%
 
/en/players/8778c910/Manuel-Neuer71.1%
 
/en/players/1ddbb0da/Iker-Casillas71.1%
 
/en/players/a39bb753/Eden-Hazard71.1%
 
/en/players/f20f347f/Luis-Figo71.1%
 
/en/players/f07be45a/Wayne-Rooney71.1%
 
/en/players/08511d65/Sergio-Ramos71.1%
 
/en/players/a6154613/Luis-Suarez71.1%
 
/en/players/3c6089ab/Thomas-Muller71.1%
 
/en/players/c204f844/Yaya-Toure71.1%
 
/en/players/5c7f3153/Rivaldo61.0%
 
/en/players/e46012d4/Kevin-De-Bruyne61.0%
 
/en/players/80bc263c/Alessandro-Del-Piero61.0%
 
/en/players/b2d6ea70/Kaka61.0%
 
/en/players/71c5f16f/Andrea-Pirlo61.0%
 
Other values (200)41767.4%
 
ValueCountFrequency (%) 
619100.0%
 
ValueCountFrequency (%) 
619100.0%
 

xG_Expected
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct46
Distinct (%)85.2%
Missing565
Missing (%)91.3%
Infinite0
Infinite (%)0.0%
Mean10.52407407
Minimum0
Maximum28.4
Zeros7
Zeros (%)1.1%
Memory size5.0 KiB
2021-11-29T18:41:54.254751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.725
median9.75
Q317.425
95-th percentile25.44
Maximum28.4
Range28.4
Interquartile range (IQR)15.7

Descriptive statistics

Standard deviation9.018240116
Coefficient of variation (CV)0.8569153022
Kurtosis-1.148386716
Mean10.52407407
Median Absolute Deviation (MAD)7.95
Skewness0.4229087877
Sum568.3
Variance81.32865479
MonotocityNot monotonic
2021-11-29T18:41:54.369916image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%) 
071.1%
 
6.620.3%
 
2.120.3%
 
14.510.2%
 
11.410.2%
 
3.110.2%
 
22.710.2%
 
17.210.2%
 
18.610.2%
 
27.310.2%
 
28.410.2%
 
5.710.2%
 
13.810.2%
 
19.610.2%
 
510.2%
 
13.310.2%
 
13.710.2%
 
1.410.2%
 
20.110.2%
 
7.710.2%
 
3.210.2%
 
15.810.2%
 
2.810.2%
 
23.410.2%
 
1.510.2%
 
Other values (21)213.4%
 
(Missing)56591.3%
 
ValueCountFrequency (%) 
071.1%
 
0.210.2%
 
0.810.2%
 
0.910.2%
 
1.210.2%
 
1.410.2%
 
1.510.2%
 
1.610.2%
 
2.120.3%
 
2.810.2%
 
ValueCountFrequency (%) 
28.410.2%
 
27.310.2%
 
25.710.2%
 
25.310.2%
 
24.410.2%
 
24.210.2%
 
23.410.2%
 
23.210.2%
 
22.710.2%
 
21.710.2%
 

npxG_Expected
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct41
Distinct (%)75.9%
Missing565
Missing (%)91.3%
Infinite0
Infinite (%)0.0%
Mean9.446296296
Minimum0
Maximum25.8
Zeros7
Zeros (%)1.1%
Memory size5.0 KiB
2021-11-29T18:41:54.493346image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.725
median8.05
Q315.775
95-th percentile23.075
Maximum25.8
Range25.8
Interquartile range (IQR)14.05

Descriptive statistics

Standard deviation8.025283466
Coefficient of variation (CV)0.8495693141
Kurtosis-1.054137592
Mean9.446296296
Median Absolute Deviation (MAD)6.5
Skewness0.4453486599
Sum510.1
Variance64.4051747
MonotocityNot monotonic
2021-11-29T18:41:54.609437image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%) 
071.1%
 
2.120.3%
 
2120.3%
 
19.720.3%
 
11.420.3%
 
18.120.3%
 
6.620.3%
 
520.3%
 
8.410.2%
 
13.810.2%
 
25.410.2%
 
1.510.2%
 
12.510.2%
 
1.410.2%
 
16.310.2%
 
17.210.2%
 
16.210.2%
 
7.710.2%
 
3.110.2%
 
3.210.2%
 
13.510.2%
 
2.810.2%
 
25.810.2%
 
10.310.2%
 
1.610.2%
 
Other values (16)162.6%
 
(Missing)56591.3%
 
ValueCountFrequency (%) 
071.1%
 
0.210.2%
 
0.810.2%
 
0.910.2%
 
1.210.2%
 
1.410.2%
 
1.510.2%
 
1.610.2%
 
2.120.3%
 
2.810.2%
 
ValueCountFrequency (%) 
25.810.2%
 
25.410.2%
 
23.410.2%
 
22.910.2%
 
22.310.2%
 
2120.3%
 
19.720.3%
 
18.120.3%
 
17.210.2%
 
16.310.2%
 

xA_Expected
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct40
Distinct (%)74.1%
Missing565
Missing (%)91.3%
Infinite0
Infinite (%)0.0%
Mean4.831481481
Minimum0
Maximum13.6
Zeros8
Zeros (%)1.3%
Memory size5.0 KiB
2021-11-29T18:41:54.721985image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.125
median4.85
Q37.35
95-th percentile12.36
Maximum13.6
Range13.6
Interquartile range (IQR)6.225

Descriptive statistics

Standard deviation3.788304946
Coefficient of variation (CV)0.784087647
Kurtosis-0.2595170594
Mean4.831481481
Median Absolute Deviation (MAD)2.95
Skewness0.5228663431
Sum260.9
Variance14.35125437
MonotocityNot monotonic
2021-11-29T18:41:54.837467image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%) 
081.3%
 
5.330.5%
 
4.220.3%
 
13.620.3%
 
4.920.3%
 
5.820.3%
 
7.420.3%
 
3.810.2%
 
4.510.2%
 
8.810.2%
 
5.510.2%
 
8.310.2%
 
3.410.2%
 
9.210.2%
 
9.310.2%
 
6.810.2%
 
6.310.2%
 
3.310.2%
 
0.210.2%
 
5.610.2%
 
1.210.2%
 
1.110.2%
 
0.810.2%
 
0.110.2%
 
2.410.2%
 
Other values (15)152.4%
 
(Missing)56591.3%
 
ValueCountFrequency (%) 
081.3%
 
0.110.2%
 
0.210.2%
 
0.810.2%
 
0.910.2%
 
110.2%
 
1.110.2%
 
1.210.2%
 
1.910.2%
 
2.410.2%
 
ValueCountFrequency (%) 
13.620.3%
 
13.410.2%
 
11.810.2%
 
9.710.2%
 
9.310.2%
 
9.210.2%
 
910.2%
 
8.810.2%
 
8.310.2%
 
7.910.2%
 

npxG+xA_Expected
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct47
Distinct (%)87.0%
Missing565
Missing (%)91.3%
Infinite0
Infinite (%)0.0%
Mean14.29074074
Minimum0
Maximum35.9
Zeros7
Zeros (%)1.1%
Memory size5.0 KiB
2021-11-29T18:41:54.957122image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.625
median15.4
Q322.2
95-th percentile32.39
Maximum35.9
Range35.9
Interquartile range (IQR)18.575

Descriptive statistics

Standard deviation10.76430629
Coefficient of variation (CV)0.753236413
Kurtosis-1.095061494
Mean14.29074074
Median Absolute Deviation (MAD)9.4
Skewness0.1942946909
Sum771.7
Variance115.87029
MonotocityNot monotonic
2021-11-29T18:41:55.074580image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%) 
071.1%
 
21.820.3%
 
2310.2%
 
33.310.2%
 
4.210.2%
 
21.910.2%
 
25.110.2%
 
31.310.2%
 
33.710.2%
 
13.910.2%
 
9.210.2%
 
17.710.2%
 
1.610.2%
 
5.610.2%
 
23.410.2%
 
18.410.2%
 
18.210.2%
 
7.710.2%
 
20.410.2%
 
11.110.2%
 
3.510.2%
 
19.110.2%
 
410.2%
 
10.810.2%
 
8.710.2%
 
Other values (22)223.6%
 
(Missing)56591.3%
 
ValueCountFrequency (%) 
071.1%
 
0.210.2%
 
1.610.2%
 
1.910.2%
 
310.2%
 
3.110.2%
 
3.210.2%
 
3.510.2%
 
410.2%
 
4.210.2%
 
ValueCountFrequency (%) 
35.910.2%
 
33.710.2%
 
33.310.2%
 
31.910.2%
 
31.310.2%
 
30.710.2%
 
26.310.2%
 
25.310.2%
 
25.110.2%
 
24.510.2%
 

xG_Per
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct37
Distinct (%)68.5%
Missing565
Missing (%)91.3%
Infinite0
Infinite (%)0.0%
Mean0.3777777778
Minimum0
Maximum1.05
Zeros7
Zeros (%)1.1%
Memory size5.0 KiB
2021-11-29T18:41:55.197886image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.065
median0.365
Q30.6575
95-th percentile0.831
Maximum1.05
Range1.05
Interquartile range (IQR)0.5925

Descriptive statistics

Standard deviation0.3055132809
Coefficient of variation (CV)0.8087116258
Kurtosis-1.087401883
Mean0.3777777778
Median Absolute Deviation (MAD)0.305
Skewness0.342672645
Sum20.4
Variance0.09333836478
MonotocityNot monotonic
2021-11-29T18:41:55.324720image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%) 
071.1%
 
0.7630.5%
 
0.3420.3%
 
0.0620.3%
 
0.7820.3%
 
0.6720.3%
 
0.0420.3%
 
0.0920.3%
 
0.4520.3%
 
0.2120.3%
 
0.0520.3%
 
0.2510.2%
 
0.7210.2%
 
0.210.2%
 
0.3910.2%
 
0.3810.2%
 
0.8710.2%
 
1.0510.2%
 
0.5110.2%
 
0.510.2%
 
0.0810.2%
 
0.4610.2%
 
0.3510.2%
 
0.2410.2%
 
0.4210.2%
 
Other values (12)121.9%
 
(Missing)56591.3%
 
ValueCountFrequency (%) 
071.1%
 
0.0210.2%
 
0.0420.3%
 
0.0520.3%
 
0.0620.3%
 
0.0810.2%
 
0.0920.3%
 
0.1810.2%
 
0.210.2%
 
0.2120.3%
 
ValueCountFrequency (%) 
1.0510.2%
 
0.9510.2%
 
0.8710.2%
 
0.8110.2%
 
0.810.2%
 
0.7820.3%
 
0.7630.5%
 
0.7510.2%
 
0.7210.2%
 
0.6720.3%
 

xA_Per
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct28
Distinct (%)51.9%
Missing565
Missing (%)91.3%
Infinite0
Infinite (%)0.0%
Mean0.1774074074
Minimum0
Maximum0.59
Zeros9
Zeros (%)1.5%
Memory size5.0 KiB
2021-11-29T18:41:55.435254image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.04
median0.19
Q30.28
95-th percentile0.397
Maximum0.59
Range0.59
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.1372669234
Coefficient of variation (CV)0.7737383988
Kurtosis0.08638776377
Mean0.1774074074
Median Absolute Deviation (MAD)0.11
Skewness0.4755059489
Sum9.58
Variance0.01884220825
MonotocityNot monotonic
2021-11-29T18:41:55.535103image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%) 
091.5%
 
0.1740.6%
 
0.1940.6%
 
0.2830.5%
 
0.0430.5%
 
0.330.5%
 
0.2320.3%
 
0.1320.3%
 
0.2920.3%
 
0.220.3%
 
0.2420.3%
 
0.2520.3%
 
0.3410.2%
 
0.5910.2%
 
0.0710.2%
 
0.0810.2%
 
0.0210.2%
 
0.3910.2%
 
0.4510.2%
 
0.0310.2%
 
0.4110.2%
 
0.2110.2%
 
0.3310.2%
 
0.3210.2%
 
0.1410.2%
 
Other values (3)30.5%
 
(Missing)56591.3%
 
ValueCountFrequency (%) 
091.5%
 
0.0110.2%
 
0.0210.2%
 
0.0310.2%
 
0.0430.5%
 
0.0610.2%
 
0.0710.2%
 
0.0810.2%
 
0.1320.3%
 
0.1410.2%
 
ValueCountFrequency (%) 
0.5910.2%
 
0.4510.2%
 
0.4110.2%
 
0.3910.2%
 
0.3410.2%
 
0.3310.2%
 
0.3210.2%
 
0.330.5%
 
0.2920.3%
 
0.2830.5%
 

xG+xA_Per
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct39
Distinct (%)72.2%
Missing565
Missing (%)91.3%
Infinite0
Infinite (%)0.0%
Mean0.5566666667
Minimum0
Maximum1.37
Zeros7
Zeros (%)1.1%
Memory size5.0 KiB
2021-11-29T18:41:55.649464image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.115
median0.605
Q30.8375
95-th percentile1.191
Maximum1.37
Range1.37
Interquartile range (IQR)0.7225

Descriptive statistics

Standard deviation0.4011681057
Coefficient of variation (CV)0.7206612678
Kurtosis-1.056930535
Mean0.5566666667
Median Absolute Deviation (MAD)0.34
Skewness0.113732333
Sum30.06
Variance0.1609358491
MonotocityNot monotonic
2021-11-29T18:41:55.759528image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%) 
071.1%
 
0.6440.6%
 
0.5720.3%
 
1.1220.3%
 
0.2820.3%
 
0.9820.3%
 
0.120.3%
 
0.1120.3%
 
0.9110.2%
 
0.6610.2%
 
1.2310.2%
 
0.5810.2%
 
0.810.2%
 
0.7510.2%
 
0.6710.2%
 
0.4410.2%
 
1.2610.2%
 
0.4910.2%
 
0.7610.2%
 
0.6510.2%
 
0.8910.2%
 
0.5210.2%
 
0.0910.2%
 
0.5510.2%
 
0.0610.2%
 
Other values (14)142.3%
 
(Missing)56591.3%
 
ValueCountFrequency (%) 
071.1%
 
0.0610.2%
 
0.0710.2%
 
0.0910.2%
 
0.120.3%
 
0.1120.3%
 
0.1310.2%
 
0.2110.2%
 
0.2820.3%
 
0.3510.2%
 
ValueCountFrequency (%) 
1.3710.2%
 
1.2610.2%
 
1.2310.2%
 
1.1710.2%
 
1.1410.2%
 
1.1220.3%
 
1.0310.2%
 
0.9820.3%
 
0.9610.2%
 
0.9110.2%
 

npxG_Per
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct37
Distinct (%)68.5%
Missing565
Missing (%)91.3%
Infinite0
Infinite (%)0.0%
Mean0.3388888889
Minimum0
Maximum0.99
Zeros7
Zeros (%)1.1%
Memory size5.0 KiB
2021-11-29T18:41:55.882887image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.065
median0.325
Q30.595
95-th percentile0.731
Maximum0.99
Range0.99
Interquartile range (IQR)0.53

Descriptive statistics

Standard deviation0.270112951
Coefficient of variation (CV)0.7970546095
Kurtosis-0.9707713289
Mean0.3388888889
Median Absolute Deviation (MAD)0.265
Skewness0.3452951074
Sum18.3
Variance0.07296100629
MonotocityNot monotonic
2021-11-29T18:41:55.994576image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%) 
071.1%
 
0.2820.3%
 
0.4520.3%
 
0.0520.3%
 
0.0620.3%
 
0.6620.3%
 
0.6920.3%
 
0.6520.3%
 
0.0420.3%
 
0.6220.3%
 
0.2120.3%
 
0.0920.3%
 
0.3210.2%
 
0.4310.2%
 
0.6110.2%
 
0.5310.2%
 
0.3410.2%
 
0.210.2%
 
0.2610.2%
 
0.3310.2%
 
0.7710.2%
 
0.9910.2%
 
0.510.2%
 
0.4210.2%
 
0.0810.2%
 
Other values (12)121.9%
 
(Missing)56591.3%
 
ValueCountFrequency (%) 
071.1%
 
0.0210.2%
 
0.0420.3%
 
0.0520.3%
 
0.0620.3%
 
0.0810.2%
 
0.0920.3%
 
0.1510.2%
 
0.1810.2%
 
0.210.2%
 
ValueCountFrequency (%) 
0.9910.2%
 
0.8310.2%
 
0.7710.2%
 
0.7110.2%
 
0.6920.3%
 
0.6710.2%
 
0.6620.3%
 
0.6520.3%
 
0.6220.3%
 
0.6110.2%
 

npxG+xA_Per
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct42
Distinct (%)77.8%
Missing565
Missing (%)91.3%
Infinite0
Infinite (%)0.0%
Mean0.517962963
Minimum0
Maximum1.22
Zeros7
Zeros (%)1.1%
Memory size5.0 KiB
2021-11-29T18:41:56.108722image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.115
median0.56
Q30.765
95-th percentile1.087
Maximum1.22
Range1.22
Interquartile range (IQR)0.65

Descriptive statistics

Standard deviation0.3672610486
Coefficient of variation (CV)0.7090488604
Kurtosis-1.084498564
Mean0.517962963
Median Absolute Deviation (MAD)0.325
Skewness0.06405676683
Sum27.97
Variance0.1348806778
MonotocityNot monotonic
2021-11-29T18:41:56.228722image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%) 
071.1%
 
0.5820.3%
 
0.1120.3%
 
0.5520.3%
 
120.3%
 
0.120.3%
 
0.6320.3%
 
0.0710.2%
 
0.6210.2%
 
0.7310.2%
 
0.6410.2%
 
0.710.2%
 
1.210.2%
 
1.0310.2%
 
0.4410.2%
 
0.7510.2%
 
1.110.2%
 
0.5210.2%
 
0.8610.2%
 
0.5110.2%
 
0.2810.2%
 
0.6710.2%
 
0.4910.2%
 
0.5910.2%
 
0.2110.2%
 
Other values (17)172.7%
 
(Missing)56591.3%
 
ValueCountFrequency (%) 
071.1%
 
0.0610.2%
 
0.0710.2%
 
0.0910.2%
 
0.120.3%
 
0.1120.3%
 
0.1310.2%
 
0.1910.2%
 
0.2110.2%
 
0.2810.2%
 
ValueCountFrequency (%) 
1.2210.2%
 
1.210.2%
 
1.110.2%
 
1.0810.2%
 
1.0310.2%
 
120.3%
 
0.9910.2%
 
0.9610.2%
 
0.9510.2%
 
0.8610.2%
 

BO_JK
Categorical

HIGH CARDINALITY
UNIFORM

Distinct569
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2005Adriano
 
3
1996Alan Shearer
 
2
2012Xabi Alonso
 
2
2018Marcelo
 
2
2012Mario Balotelli
 
2
Other values (564)
608 
ValueCountFrequency (%) 
2005Adriano30.5%
 
1996Alan Shearer20.3%
 
2012Xabi Alonso20.3%
 
2018Marcelo20.3%
 
2012Mario Balotelli20.3%
 
2012Karim Benzema20.3%
 
2012Gerard Piqué20.3%
 
2012Sergio Busquets20.3%
 
2012Sergio Ramos20.3%
 
2012Sergio Agüero20.3%
 
2012Gianluigi Buffon20.3%
 
2012Wayne Rooney20.3%
 
2012Mesut Özil20.3%
 
2012Yaya Touré20.3%
 
2012Robin van Persie20.3%
 
2013Andrés Iniesta20.3%
 
2012Didier Drogba20.3%
 
2012Andrea Pirlo20.3%
 
2012Iker Casillas20.3%
 
2012Radamel Falcao20.3%
 
2012Xavi20.3%
 
2018Kylian Mbappé20.3%
 
2004Adriano20.3%
 
1999Nwankwo Kanu20.3%
 
2016Kevin De Bruyne20.3%
 
Other values (544)56891.8%
 
2021-11-29T18:41:56.363925image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique520 ?
Unique (%)84.0%
2021-11-29T18:41:56.502917image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length29
Median length17
Mean length16.75767367
Min length7

Overview of Unicode Properties

Unique unicode characters79
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
07887.6%
 
a7677.4%
 
e6776.5%
 
i6216.0%
 
5895.7%
 
25705.5%
 
r5305.1%
 
n5295.1%
 
o5215.0%
 
14314.2%
 
l3583.5%
 
s3002.9%
 
92982.9%
 
d2382.3%
 
u1901.8%
 
t1881.8%
 
m1241.2%
 
h1221.2%
 
g1201.2%
 
R1201.2%
 
y1021.0%
 
c1001.0%
 
b970.9%
 
M950.9%
 
7910.9%
 
Other values (54)180717.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter609258.7%
 
Decimal Number247623.9%
 
Uppercase Letter120011.6%
 
Space Separator5895.7%
 
Other Punctuation100.1%
 
Dash Punctuation60.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
078831.8%
 
257023.0%
 
143117.4%
 
929812.0%
 
7913.7%
 
8783.2%
 
6753.0%
 
3592.4%
 
4441.8%
 
5421.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R12010.0%
 
M957.9%
 
D907.5%
 
S806.7%
 
B776.4%
 
A766.3%
 
C665.5%
 
L665.5%
 
P635.2%
 
G605.0%
 
F564.7%
 
K534.4%
 
T514.2%
 
E383.2%
 
H292.4%
 
N272.2%
 
J262.2%
 
Z262.2%
 
I221.8%
 
V211.8%
 
O131.1%
 
X121.0%
 
Y110.9%
 
W110.9%
 
Ö60.5%
 
Other values (3)50.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a76712.6%
 
e67711.1%
 
i62110.2%
 
r5308.7%
 
n5298.7%
 
o5218.6%
 
l3585.9%
 
s3004.9%
 
d2383.9%
 
u1903.1%
 
t1883.1%
 
m1242.0%
 
h1222.0%
 
g1202.0%
 
y1021.7%
 
c1001.6%
 
b971.6%
 
v901.5%
 
k721.2%
 
z591.0%
 
é550.9%
 
p480.8%
 
f420.7%
 
w260.4%
 
á260.4%
 
Other values (13)901.5%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
589100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
'10100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-6100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin729270.3%
 
Common308129.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
078825.6%
 
58919.1%
 
257018.5%
 
143114.0%
 
92989.7%
 
7913.0%
 
8782.5%
 
6752.4%
 
3591.9%
 
4441.4%
 
5421.4%
 
'100.3%
 
-60.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a76710.5%
 
e6779.3%
 
i6218.5%
 
r5307.3%
 
n5297.3%
 
o5217.1%
 
l3584.9%
 
s3004.1%
 
d2383.3%
 
u1902.6%
 
t1882.6%
 
m1241.7%
 
h1221.7%
 
g1201.6%
 
R1201.6%
 
y1021.4%
 
c1001.4%
 
b971.3%
 
M951.3%
 
D901.2%
 
v901.2%
 
S801.1%
 
B771.1%
 
A761.0%
 
k721.0%
 
Other values (41)100813.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1021998.5%
 
None1541.5%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
07887.7%
 
a7677.5%
 
e6776.6%
 
i6216.1%
 
5895.8%
 
25705.6%
 
r5305.2%
 
n5295.2%
 
o5215.1%
 
14314.2%
 
l3583.5%
 
s3002.9%
 
92982.9%
 
d2382.3%
 
u1901.9%
 
t1881.8%
 
m1241.2%
 
h1221.2%
 
g1201.2%
 
R1201.2%
 
y1021.0%
 
c1001.0%
 
b970.9%
 
M950.9%
 
7910.9%
 
Other values (38)165316.2%
 

Most frequent None characters

ValueCountFrequency (%) 
é5535.7%
 
á2616.9%
 
ü1811.7%
 
í159.7%
 
ú106.5%
 
ó63.9%
 
Ö63.9%
 
à42.6%
 
ö31.9%
 
Š21.3%
 
ä21.3%
 
É21.3%
 
ž21.3%
 
š10.6%
 
Á10.6%
 
ë10.6%
 

Nom
Categorical

HIGH CARDINALITY

Distinct221
Distinct (%)35.7%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
Cristiano Ronaldo
 
16
Lionel Messi
 
14
Zinédine Zidane
 
11
Thierry Henry
 
9
Gianluigi Buffon
 
9
Other values (216)
560 
ValueCountFrequency (%) 
Cristiano Ronaldo162.6%
 
Lionel Messi142.3%
 
Zinédine Zidane111.8%
 
Thierry Henry91.5%
 
Gianluigi Buffon91.5%
 
Didier Drogba91.5%
 
Andrés Iniesta91.5%
 
Karim Benzema81.3%
 
Xavi81.3%
 
Sergio Agüero81.3%
 
Samuel Eto'o81.3%
 
Sergio Ramos71.1%
 
Manuel Neuer71.1%
 
Yaya Touré71.1%
 
Luis Suárez71.1%
 
Luís Figo71.1%
 
Eden Hazard71.1%
 
Thomas Müller71.1%
 
Kaká71.1%
 
Wayne Rooney71.1%
 
Iker Casillas71.1%
 
David Beckham61.0%
 
Rivaldo61.0%
 
Alessandro Del Piero61.0%
 
Andrea Pirlo61.0%
 
Other values (196)41667.2%
 
2021-11-29T18:41:56.658693image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique91 ?
Unique (%)14.7%
2021-11-29T18:41:56.802144image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length13
Mean length12.75767367
Min length3

Overview of Unicode Properties

Unique unicode characters69
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a7679.7%
 
e6778.6%
 
i6217.9%
 
5897.5%
 
r5306.7%
 
n5296.7%
 
o5216.6%
 
l3584.5%
 
s3003.8%
 
d2383.0%
 
u1902.4%
 
t1882.4%
 
m1241.6%
 
h1221.5%
 
g1201.5%
 
R1201.5%
 
y1021.3%
 
c1001.3%
 
b971.2%
 
M951.2%
 
D901.1%
 
v901.1%
 
S801.0%
 
B771.0%
 
A761.0%
 
Other values (44)109613.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter609277.1%
 
Uppercase Letter120015.2%
 
Space Separator5897.5%
 
Other Punctuation100.1%
 
Dash Punctuation60.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R12010.0%
 
M957.9%
 
D907.5%
 
S806.7%
 
B776.4%
 
A766.3%
 
C665.5%
 
L665.5%
 
P635.2%
 
G605.0%
 
F564.7%
 
K534.4%
 
T514.2%
 
E383.2%
 
H292.4%
 
N272.2%
 
J262.2%
 
Z262.2%
 
I221.8%
 
V211.8%
 
O131.1%
 
X121.0%
 
Y110.9%
 
W110.9%
 
Ö60.5%
 
Other values (3)50.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a76712.6%
 
e67711.1%
 
i62110.2%
 
r5308.7%
 
n5298.7%
 
o5218.6%
 
l3585.9%
 
s3004.9%
 
d2383.9%
 
u1903.1%
 
t1883.1%
 
m1242.0%
 
h1222.0%
 
g1202.0%
 
y1021.7%
 
c1001.6%
 
b971.6%
 
v901.5%
 
k721.2%
 
z591.0%
 
é550.9%
 
p480.8%
 
f420.7%
 
w260.4%
 
á260.4%
 
Other values (13)901.5%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
589100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
'10100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-6100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin729292.3%
 
Common6057.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a76710.5%
 
e6779.3%
 
i6218.5%
 
r5307.3%
 
n5297.3%
 
o5217.1%
 
l3584.9%
 
s3004.1%
 
d2383.3%
 
u1902.6%
 
t1882.6%
 
m1241.7%
 
h1221.7%
 
g1201.6%
 
R1201.6%
 
y1021.4%
 
c1001.4%
 
b971.3%
 
M951.3%
 
D901.2%
 
v901.2%
 
S801.1%
 
B771.1%
 
A761.0%
 
k721.0%
 
Other values (41)100813.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
58997.4%
 
'101.7%
 
-61.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII774398.0%
 
None1542.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a7679.9%
 
e6778.7%
 
i6218.0%
 
5897.6%
 
r5306.8%
 
n5296.8%
 
o5216.7%
 
l3584.6%
 
s3003.9%
 
d2383.1%
 
u1902.5%
 
t1882.4%
 
m1241.6%
 
h1221.6%
 
g1201.5%
 
R1201.5%
 
y1021.3%
 
c1001.3%
 
b971.3%
 
M951.2%
 
D901.2%
 
v901.2%
 
S801.0%
 
B771.0%
 
A761.0%
 
Other values (28)94212.2%
 

Most frequent None characters

ValueCountFrequency (%) 
é5535.7%
 
á2616.9%
 
ü1811.7%
 
í159.7%
 
ú106.5%
 
ó63.9%
 
Ö63.9%
 
à42.6%
 
ö31.9%
 
Š21.3%
 
ä21.3%
 
É21.3%
 
ž21.3%
 
š10.6%
 
Á10.6%
 
ë10.6%
 

Pays
Categorical

HIGH CORRELATION

Distinct41
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
France
79 
Espagne
77 
Allemagne
65 
Brésil
63 
Italie
58 
Other values (36)
277 
ValueCountFrequency (%) 
France7912.8%
 
Espagne7712.4%
 
Allemagne6510.5%
 
Brésil6310.2%
 
Italie589.4%
 
Argentine436.9%
 
Angleterre416.6%
 
Pays-Bas345.5%
 
Portugal304.8%
 
Belgique162.6%
 
Uruguay162.6%
 
Côte d’Ivoire121.9%
 
Pays de Galles101.6%
 
Cameroun81.3%
 
Colombie71.1%
 
Pologne61.0%
 
Sénégal50.8%
 
Chili40.6%
 
Côte d'Ivoire40.6%
 
République tchèque30.5%
 
Danemark30.5%
 
Ghana30.5%
 
Gabon30.5%
 
Nigeria30.5%
 
Grèce20.3%
 
Other values (16)243.9%
 
2021-11-29T18:41:56.951772image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique8 ?
Unique (%)1.3%
2021-11-29T18:41:57.085449image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length7
Mean length7.765751212
Min length4

Overview of Unicode Properties

Unique unicode characters47
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e70114.6%
 
a4559.5%
 
l3958.2%
 
n3888.1%
 
r3597.5%
 
g3126.5%
 
i2445.1%
 
s2304.8%
 
t1974.1%
 
A1513.1%
 
B1172.4%
 
u1062.2%
 
o972.0%
 
é881.8%
 
c861.8%
 
m841.7%
 
p841.7%
 
F801.7%
 
P801.7%
 
E771.6%
 
I761.6%
 
y621.3%
 
430.9%
 
C380.8%
 
-380.8%
 
Other values (22)2194.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter402383.7%
 
Uppercase Letter68514.3%
 
Space Separator430.9%
 
Dash Punctuation380.8%
 
Final Punctuation120.2%
 
Other Punctuation60.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A15122.0%
 
B11717.1%
 
F8011.7%
 
P8011.7%
 
E7711.2%
 
I7611.1%
 
C385.5%
 
G182.6%
 
U162.3%
 
S91.3%
 
R60.9%
 
T50.7%
 
D30.4%
 
N30.4%
 
H20.3%
 
É20.3%
 
L10.1%
 
M10.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e70117.4%
 
a45511.3%
 
l3959.8%
 
n3889.6%
 
r3598.9%
 
g3127.8%
 
i2446.1%
 
s2305.7%
 
t1974.9%
 
u1062.6%
 
o972.4%
 
é882.2%
 
c862.1%
 
m842.1%
 
p842.1%
 
y621.5%
 
d330.8%
 
q260.6%
 
v200.5%
 
b180.4%
 
ô160.4%
 
h120.3%
 
è50.1%
 
k30.1%
 
z2< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
43100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-38100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
'6100.0%
 

Most frequent Final Punctuation characters

ValueCountFrequency (%) 
12100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin470897.9%
 
Common992.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e70114.9%
 
a4559.7%
 
l3958.4%
 
n3888.2%
 
r3597.6%
 
g3126.6%
 
i2445.2%
 
s2304.9%
 
t1974.2%
 
A1513.2%
 
B1172.5%
 
u1062.3%
 
o972.1%
 
é881.9%
 
c861.8%
 
m841.8%
 
p841.8%
 
F801.7%
 
P801.7%
 
E771.6%
 
I761.6%
 
y621.3%
 
C380.8%
 
d330.7%
 
q260.6%
 
Other values (18)1423.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
4343.4%
 
-3838.4%
 
1212.1%
 
'66.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII468497.4%
 
None1112.3%
 
Punctuation120.2%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e70115.0%
 
a4559.7%
 
l3958.4%
 
n3888.3%
 
r3597.7%
 
g3126.7%
 
i2445.2%
 
s2304.9%
 
t1974.2%
 
A1513.2%
 
B1172.5%
 
u1062.3%
 
o972.1%
 
c861.8%
 
m841.8%
 
p841.8%
 
F801.7%
 
P801.7%
 
E771.6%
 
I761.6%
 
y621.3%
 
430.9%
 
C380.8%
 
-380.8%
 
d330.7%
 
Other values (17)1513.2%
 

Most frequent None characters

ValueCountFrequency (%) 
é8879.3%
 
ô1614.4%
 
è54.5%
 
É21.8%
 

Most frequent Punctuation characters

ValueCountFrequency (%) 
12100.0%
 

Club
Categorical

HIGH CARDINALITY

Distinct138
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
Real Madrid
81 
FC Barcelone
71 
Bayern Munich
54 
Manchester United
36 
Juventus
 
24
Other values (133)
353 
ValueCountFrequency (%) 
Real Madrid8113.1%
 
FC Barcelone7111.5%
 
Bayern Munich548.7%
 
Manchester United365.8%
 
Juventus243.9%
 
Chelsea233.7%
 
Manchester City223.6%
 
Milan AC193.1%
 
Arsenal162.6%
 
Liverpool FC152.4%
 
Inter Milan132.1%
 
Liverpool121.9%
 
Paris Saint-Germain111.8%
 
Borussia Dortmund101.6%
 
Atlético Madrid91.5%
 
Arsenal FC71.1%
 
Juventus de Turin71.1%
 
Tottenham Hotspur71.1%
 
Chelsea FC61.0%
 
Valence CF61.0%
 
AS Roma61.0%
 
Juventus FC50.8%
 
Lazio Rome50.8%
 
Fiorentina AC40.6%
 
Milan40.6%
 
Other values (113)14623.6%
 
2021-11-29T18:41:57.228625image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique83 ?
Unique (%)13.4%
2021-11-29T18:41:57.362657image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length44
Median length12
Mean length13.84006462
Min length4

Overview of Unicode Properties

Unique unicode characters55
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e93810.9%
 
a7668.9%
 
7238.4%
 
n6197.2%
 
r5806.8%
 
i5116.0%
 
l4465.2%
 
t3614.2%
 
d3564.2%
 
M3394.0%
 
o3133.7%
 
c2813.3%
 
s2753.2%
 
C2643.1%
 
u2252.6%
 
h1882.2%
 
B1732.0%
 
F1611.9%
 
R1261.5%
 
A1061.2%
 
y981.1%
 
v951.1%
 
m700.8%
 
S610.7%
 
L560.7%
 
Other values (30)4365.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter626673.1%
 
Uppercase Letter154218.0%
 
Space Separator7238.4%
 
Other Punctuation170.2%
 
Dash Punctuation150.2%
 
Decimal Number4< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M33922.0%
 
C26417.1%
 
B17311.2%
 
F16110.4%
 
R1268.2%
 
A1066.9%
 
S614.0%
 
L563.6%
 
U543.5%
 
J523.4%
 
P241.6%
 
I231.5%
 
G201.3%
 
T181.2%
 
V151.0%
 
D140.9%
 
H100.6%
 
N80.5%
 
W70.5%
 
O50.3%
 
K40.3%
 
E20.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e93815.0%
 
a76612.2%
 
n6199.9%
 
r5809.3%
 
i5118.2%
 
l4467.1%
 
t3615.8%
 
d3565.7%
 
o3135.0%
 
c2814.5%
 
s2754.4%
 
u2253.6%
 
h1883.0%
 
y981.6%
 
v951.5%
 
m701.1%
 
p530.8%
 
é260.4%
 
z120.2%
 
b110.2%
 
g110.2%
 
k90.1%
 
f50.1%
 
q50.1%
 
ê40.1%
 
Other values (3)80.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
723100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/17100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0250.0%
 
4250.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-15100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin780891.1%
 
Common7598.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e93812.0%
 
a7669.8%
 
n6197.9%
 
r5807.4%
 
i5116.5%
 
l4465.7%
 
t3614.6%
 
d3564.6%
 
M3394.3%
 
o3134.0%
 
c2813.6%
 
s2753.5%
 
C2643.4%
 
u2252.9%
 
h1882.4%
 
B1732.2%
 
F1612.1%
 
R1261.6%
 
A1061.4%
 
y981.3%
 
v951.2%
 
m700.9%
 
S610.8%
 
L560.7%
 
U540.7%
 
Other values (25)3464.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
72395.3%
 
/172.2%
 
-152.0%
 
020.3%
 
420.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII853799.6%
 
None300.4%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e93811.0%
 
a7669.0%
 
7238.5%
 
n6197.3%
 
r5806.8%
 
i5116.0%
 
l4465.2%
 
t3614.2%
 
d3564.2%
 
M3394.0%
 
o3133.7%
 
c2813.3%
 
s2753.2%
 
C2643.1%
 
u2252.6%
 
h1882.2%
 
B1732.0%
 
F1611.9%
 
R1261.5%
 
A1061.2%
 
y981.1%
 
v951.1%
 
m700.8%
 
S610.7%
 
L560.7%
 
Other values (28)4064.8%
 

Most frequent None characters

ValueCountFrequency (%) 
é2686.7%
 
ê413.3%
 

%
Categorical

HIGH CARDINALITY
MISSING

Distinct317
Distinct (%)52.3%
Missing13
Missing (%)2.1%
Memory size5.0 KiB
0.00130718954248366
 
21
0.0
 
17
0.00261437908496732
 
15
0.00641025641025641
 
12
0.00392156862745098
 
12
Other values (312)
529 
ValueCountFrequency (%) 
0.00130718954248366213.4%
 
0.0172.7%
 
0.00261437908496732152.4%
 
0.00641025641025641121.9%
 
0.00392156862745098121.9%
 
0.001282051282051282111.8%
 
0.002564102564102564101.6%
 
0.00512820512820512891.5%
 
0.003846153846153846491.5%
 
0.0052287581699346491.5%
 
0.000694444444444444581.3%
 
0.01307189542483660271.1%
 
0.002083333333333333371.1%
 
0.00138888888888888961.0%
 
0.00897435897435897461.0%
 
0.00653594771241830161.0%
 
0.01176470588235294161.0%
 
0.00347222222222222250.8%
 
0.01410256410256410350.8%
 
0.00486111111111111150.8%
 
0.0143790849673202650.8%
 
0,22 %40.6%
 
0,43 %40.6%
 
0,11 %40.6%
 
0,39 %40.6%
 
Other values (292)39964.5%
 
(Missing)132.1%
 
2021-11-29T18:41:57.509369image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique212 ?
Unique (%)35.0%
2021-11-29T18:41:58.380598image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length19
Mean length15.75444265
Min length3

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0179118.4%
 
29619.9%
 
19219.4%
 
48618.8%
 
68298.5%
 
38058.3%
 
57677.9%
 
87537.7%
 
76416.6%
 
95165.3%
 
.4754.9%
 
,1311.3%
 
 1311.3%
 
%1311.3%
 
n260.3%
 
a130.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number884590.7%
 
Other Punctuation7377.6%
 
Space Separator1311.3%
 
Lowercase Letter390.4%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0179120.2%
 
296110.9%
 
192110.4%
 
48619.7%
 
68299.4%
 
38059.1%
 
57678.7%
 
87538.5%
 
76417.2%
 
95165.8%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.47564.5%
 
,13117.8%
 
%13117.8%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
 131100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2666.7%
 
a1333.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common971399.6%
 
Latin390.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
0179118.4%
 
29619.9%
 
19219.5%
 
48618.9%
 
68298.5%
 
38058.3%
 
57677.9%
 
87537.8%
 
76416.6%
 
95165.3%
 
.4754.9%
 
,1311.3%
 
 1311.3%
 
%1311.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2666.7%
 
a1333.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII962198.7%
 
None1311.3%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0179118.6%
 
296110.0%
 
19219.6%
 
48618.9%
 
68298.6%
 
38058.4%
 
57678.0%
 
87537.8%
 
76416.7%
 
95165.4%
 
.4754.9%
 
,1311.4%
 
%1311.4%
 
n260.3%
 
a130.1%
 

Most frequent None characters

ValueCountFrequency (%) 
 131100.0%
 

UCL_JK
Categorical

HIGH CARDINALITY

Distinct304
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2013Bayern Munich
 
11
2012Real Madrid
 
11
2012Barcelona
 
8
2011Barcelona
 
7
2014Bayern Munich
 
7
Other values (299)
575 
ValueCountFrequency (%) 
2013Bayern Munich111.8%
 
2012Real Madrid111.8%
 
2012Barcelona81.3%
 
2011Barcelona71.1%
 
2014Bayern Munich71.1%
 
1999Manchester Utd71.1%
 
2019Liverpool71.1%
 
2018Real Madrid71.1%
 
2007Milan61.0%
 
2017Real Madrid61.0%
 
1997Juventus61.0%
 
2009Barcelona61.0%
 
2012Manchester City61.0%
 
2010Barcelona50.8%
 
2015Barcelona50.8%
 
2013Barcelona50.8%
 
2010Bayern Munich50.8%
 
2006Barcelona50.8%
 
2005Chelsea50.8%
 
2011Real Madrid50.8%
 
2015Real Madrid50.8%
 
1998Real Madrid50.8%
 
2016Real Madrid50.8%
 
2014Real Madrid50.8%
 
2019Manchester City50.8%
 
Other values (279)46475.0%
 
2021-11-29T18:41:58.504384image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique160 ?
Unique (%)25.8%
2021-11-29T18:41:58.614602image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length23
Median length13
Mean length13.62520194
Min length8

Overview of Unicode Properties

Unique unicode characters60
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
07909.4%
 
a6938.2%
 
e6217.4%
 
25706.8%
 
r4585.4%
 
n4505.3%
 
14315.1%
 
l3564.2%
 
i3394.0%
 
92983.5%
 
t2823.3%
 
d2773.3%
 
M2663.2%
 
2533.0%
 
c2462.9%
 
o2452.9%
 
s1982.3%
 
u1762.1%
 
h1621.9%
 
B1461.7%
 
R951.1%
 
7911.1%
 
v841.0%
 
y811.0%
 
8780.9%
 
Other values (35)7488.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter480557.0%
 
Decimal Number248029.4%
 
Uppercase Letter88410.5%
 
Space Separator2533.0%
 
Dash Punctuation120.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
079031.9%
 
257023.0%
 
143117.4%
 
929812.0%
 
7913.7%
 
8783.1%
 
6753.0%
 
3592.4%
 
4461.9%
 
5421.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M26630.1%
 
B14616.5%
 
R9510.7%
 
C606.8%
 
L495.5%
 
A485.4%
 
J465.2%
 
U434.9%
 
S212.4%
 
P192.1%
 
I151.7%
 
V131.5%
 
D121.4%
 
G121.4%
 
F101.1%
 
T101.1%
 
W80.9%
 
N60.7%
 
E20.2%
 
H20.2%
 
K10.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a69314.4%
 
e62112.9%
 
r4589.5%
 
n4509.4%
 
l3567.4%
 
i3397.1%
 
t2825.9%
 
d2775.8%
 
c2465.1%
 
o2455.1%
 
s1984.1%
 
u1763.7%
 
h1623.4%
 
v841.7%
 
y811.7%
 
m410.9%
 
p340.7%
 
é210.4%
 
z100.2%
 
k80.2%
 
b60.1%
 
g50.1%
 
f40.1%
 
ñ40.1%
 
x2< 0.1%
 
Other values (2)2< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
253100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-12100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin568967.5%
 
Common274532.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
079028.8%
 
257020.8%
 
143115.7%
 
929810.9%
 
2539.2%
 
7913.3%
 
8782.8%
 
6752.7%
 
3592.1%
 
4461.7%
 
5421.5%
 
-120.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a69312.2%
 
e62110.9%
 
r4588.1%
 
n4507.9%
 
l3566.3%
 
i3396.0%
 
t2825.0%
 
d2774.9%
 
M2664.7%
 
c2464.3%
 
o2454.3%
 
s1983.5%
 
u1763.1%
 
h1622.8%
 
B1462.6%
 
R951.7%
 
v841.5%
 
y811.4%
 
C601.1%
 
L490.9%
 
A480.8%
 
J460.8%
 
U430.8%
 
m410.7%
 
p340.6%
 
Other values (23)1933.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII840899.7%
 
None260.3%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
07909.4%
 
a6938.2%
 
e6217.4%
 
25706.8%
 
r4585.4%
 
n4505.4%
 
14315.1%
 
l3564.2%
 
i3394.0%
 
92983.5%
 
t2823.4%
 
d2773.3%
 
M2663.2%
 
2533.0%
 
c2462.9%
 
o2452.9%
 
s1982.4%
 
u1762.1%
 
h1621.9%
 
B1461.7%
 
R951.1%
 
7911.1%
 
v841.0%
 
y811.0%
 
8780.9%
 
Other values (32)7228.6%
 

Most frequent None characters

ValueCountFrequency (%) 
é2180.8%
 
ñ415.4%
 
ö13.8%
 

won_UCL
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
508 
1
111 
ValueCountFrequency (%) 
050882.1%
 
111117.9%
 
2021-11-29T18:41:58.682688image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Squad_JK
Categorical

HIGH CARDINALITY

Distinct304
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2013Bayern Munich
 
11
2012Real Madrid
 
11
2012Barcelona
 
8
2011Barcelona
 
7
2014Bayern Munich
 
7
Other values (299)
575 
ValueCountFrequency (%) 
2013Bayern Munich111.8%
 
2012Real Madrid111.8%
 
2012Barcelona81.3%
 
2011Barcelona71.1%
 
2014Bayern Munich71.1%
 
1999Manchester Utd71.1%
 
2019Liverpool71.1%
 
2018Real Madrid71.1%
 
2007Milan61.0%
 
2017Real Madrid61.0%
 
1997Juventus61.0%
 
2009Barcelona61.0%
 
2012Manchester City61.0%
 
2010Barcelona50.8%
 
2015Barcelona50.8%
 
2013Barcelona50.8%
 
2010Bayern Munich50.8%
 
2006Barcelona50.8%
 
2005Chelsea50.8%
 
2011Real Madrid50.8%
 
2015Real Madrid50.8%
 
1998Real Madrid50.8%
 
2016Real Madrid50.8%
 
2014Real Madrid50.8%
 
2019Manchester City50.8%
 
Other values (279)46475.0%
 
2021-11-29T18:41:58.775515image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique160 ?
Unique (%)25.8%
2021-11-29T18:41:58.901941image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length23
Median length13
Mean length13.62520194
Min length8

Overview of Unicode Properties

Unique unicode characters60
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
07909.4%
 
a6938.2%
 
e6217.4%
 
25706.8%
 
r4585.4%
 
n4505.3%
 
14315.1%
 
l3564.2%
 
i3394.0%
 
92983.5%
 
t2823.3%
 
d2773.3%
 
M2663.2%
 
2533.0%
 
c2462.9%
 
o2452.9%
 
s1982.3%
 
u1762.1%
 
h1621.9%
 
B1461.7%
 
R951.1%
 
7911.1%
 
v841.0%
 
y811.0%
 
8780.9%
 
Other values (35)7488.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter480557.0%
 
Decimal Number248029.4%
 
Uppercase Letter88410.5%
 
Space Separator2533.0%
 
Dash Punctuation120.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
079031.9%
 
257023.0%
 
143117.4%
 
929812.0%
 
7913.7%
 
8783.1%
 
6753.0%
 
3592.4%
 
4461.9%
 
5421.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M26630.1%
 
B14616.5%
 
R9510.7%
 
C606.8%
 
L495.5%
 
A485.4%
 
J465.2%
 
U434.9%
 
S212.4%
 
P192.1%
 
I151.7%
 
V131.5%
 
D121.4%
 
G121.4%
 
F101.1%
 
T101.1%
 
W80.9%
 
N60.7%
 
E20.2%
 
H20.2%
 
K10.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a69314.4%
 
e62112.9%
 
r4589.5%
 
n4509.4%
 
l3567.4%
 
i3397.1%
 
t2825.9%
 
d2775.8%
 
c2465.1%
 
o2455.1%
 
s1984.1%
 
u1763.7%
 
h1623.4%
 
v841.7%
 
y811.7%
 
m410.9%
 
p340.7%
 
é210.4%
 
z100.2%
 
k80.2%
 
b60.1%
 
g50.1%
 
f40.1%
 
ñ40.1%
 
x2< 0.1%
 
Other values (2)2< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
253100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-12100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin568967.5%
 
Common274532.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
079028.8%
 
257020.8%
 
143115.7%
 
929810.9%
 
2539.2%
 
7913.3%
 
8782.8%
 
6752.7%
 
3592.1%
 
4461.7%
 
5421.5%
 
-120.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a69312.2%
 
e62110.9%
 
r4588.1%
 
n4507.9%
 
l3566.3%
 
i3396.0%
 
t2825.0%
 
d2774.9%
 
M2664.7%
 
c2464.3%
 
o2454.3%
 
s1983.5%
 
u1763.1%
 
h1622.8%
 
B1462.6%
 
R951.7%
 
v841.5%
 
y811.4%
 
C601.1%
 
L490.9%
 
A480.8%
 
J460.8%
 
U430.8%
 
m410.7%
 
p340.6%
 
Other values (23)1933.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII840899.7%
 
None260.3%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
07909.4%
 
a6938.2%
 
e6217.4%
 
25706.8%
 
r4585.4%
 
n4505.4%
 
14315.1%
 
l3564.2%
 
i3394.0%
 
92983.5%
 
t2823.4%
 
d2773.3%
 
M2663.2%
 
2533.0%
 
c2462.9%
 
o2452.9%
 
s1982.4%
 
u1762.1%
 
h1621.9%
 
B1461.7%
 
R951.1%
 
7911.1%
 
v841.0%
 
y811.0%
 
8780.9%
 
Other values (32)7228.6%
 

Most frequent None characters

ValueCountFrequency (%) 
é2180.8%
 
ñ415.4%
 
ö13.8%
 

Nation_JK
Categorical

HIGH CARDINALITY

Distinct282
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2012ESP
 
13
2013GER
 
10
1998FRA
 
8
2018FRA
 
8
2010ESP
 
7
Other values (277)
573 
ValueCountFrequency (%) 
2012ESP132.1%
 
2013GER101.6%
 
1998FRA81.3%
 
2018FRA81.3%
 
2010ESP71.1%
 
2008ESP71.1%
 
2011ESP71.1%
 
2003ITA71.1%
 
1997ITA61.0%
 
2012ITA61.0%
 
2014GER61.0%
 
2007ITA61.0%
 
2006ITA61.0%
 
2009ESP61.0%
 
2000FRA61.0%
 
1998NED50.8%
 
2010GER50.8%
 
2018BRA50.8%
 
2009BRA50.8%
 
2005BRA50.8%
 
1997GER50.8%
 
1997FRA50.8%
 
1997BRA50.8%
 
2002BRA50.8%
 
1996GER50.8%
 
Other values (257)46074.3%
 
2021-11-29T18:41:59.053336image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique132 ?
Unique (%)21.3%
2021-11-29T18:41:59.165309image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length7
Min length7

Overview of Unicode Properties

Unique unicode characters33
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
078818.2%
 
257013.2%
 
14319.9%
 
R3167.3%
 
92986.9%
 
A2656.1%
 
E2495.7%
 
G1663.8%
 
P1122.6%
 
7912.1%
 
N892.1%
 
B882.0%
 
I852.0%
 
S841.9%
 
F801.8%
 
8781.8%
 
6751.7%
 
T601.4%
 
3591.4%
 
O481.1%
 
L471.1%
 
4441.0%
 
C421.0%
 
5421.0%
 
D370.9%
 
Other values (8)892.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number247657.1%
 
Uppercase Letter185742.9%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
078831.8%
 
257023.0%
 
143117.4%
 
929812.0%
 
7913.7%
 
8783.2%
 
6753.0%
 
3592.4%
 
4441.8%
 
5421.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R31617.0%
 
A26514.3%
 
E24913.4%
 
G1668.9%
 
P1126.0%
 
N894.8%
 
B884.7%
 
I854.6%
 
S844.5%
 
F804.3%
 
T603.2%
 
O482.6%
 
L472.5%
 
C422.3%
 
D372.0%
 
U351.9%
 
V181.0%
 
W100.5%
 
H90.5%
 
M90.5%
 
Z50.3%
 
Y20.1%
 
K10.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common247657.1%
 
Latin185742.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
078831.8%
 
257023.0%
 
143117.4%
 
929812.0%
 
7913.7%
 
8783.2%
 
6753.0%
 
3592.4%
 
4441.8%
 
5421.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
R31617.0%
 
A26514.3%
 
E24913.4%
 
G1668.9%
 
P1126.0%
 
N894.8%
 
B884.7%
 
I854.6%
 
S844.5%
 
F804.3%
 
T603.2%
 
O482.6%
 
L472.5%
 
C422.3%
 
D372.0%
 
U351.9%
 
V181.0%
 
W100.5%
 
H90.5%
 
M90.5%
 
Z50.3%
 
Y20.1%
 
K10.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4333100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
078818.2%
 
257013.2%
 
14319.9%
 
R3167.3%
 
92986.9%
 
A2656.1%
 
E2495.7%
 
G1663.8%
 
P1122.6%
 
7912.1%
 
N892.1%
 
B882.0%
 
I852.0%
 
S841.9%
 
F801.8%
 
8781.8%
 
6751.7%
 
T601.4%
 
3591.4%
 
O481.1%
 
L471.1%
 
4441.0%
 
C421.0%
 
5421.0%
 
D370.9%
 
Other values (8)892.1%
 

winner_Bundesliga
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
619 
ValueCountFrequency (%) 
0619100.0%
 
2021-11-29T18:41:59.225803image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

winner_C3
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
619 
ValueCountFrequency (%) 
0619100.0%
 
2021-11-29T18:41:59.249791image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

finalist_C3
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
619 
ValueCountFrequency (%) 
0619100.0%
 
2021-11-29T18:41:59.273410image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

winner_UCL
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
619 
ValueCountFrequency (%) 
0619100.0%
 
2021-11-29T18:41:59.296379image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

finalist_UCL
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
619 
ValueCountFrequency (%) 
0619100.0%
 
2021-11-29T18:41:59.320388image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

winner_Club WC
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
619 
ValueCountFrequency (%) 
0619100.0%
 
2021-11-29T18:41:59.348653image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

finalist_Club WC
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
619 
ValueCountFrequency (%) 
0619100.0%
 
2021-11-29T18:41:59.371553image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
595 
1
 
24
ValueCountFrequency (%) 
059596.1%
 
1243.9%
 
2021-11-29T18:41:59.396556image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
610 
1
 
9
ValueCountFrequency (%) 
061098.5%
 
191.5%
 
2021-11-29T18:41:59.425553image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
584 
1
 
35
ValueCountFrequency (%) 
058494.3%
 
1355.7%
 
2021-11-29T18:41:59.455592image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
602 
1
 
17
ValueCountFrequency (%) 
060297.3%
 
1172.7%
 
2021-11-29T18:41:59.484721image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

winner_Liga
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
619 
ValueCountFrequency (%) 
0619100.0%
 
2021-11-29T18:41:59.512410image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

winner_Ligue 1
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
619 
ValueCountFrequency (%) 
0619100.0%
 
2021-11-29T18:41:59.536444image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

winner_PL
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
619 
ValueCountFrequency (%) 
0619100.0%
 
2021-11-29T18:41:59.561442image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

winner_Serie A
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
619 
ValueCountFrequency (%) 
0619100.0%
 
2021-11-29T18:41:59.585079image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

winner_WC
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
579 
1
 
40
ValueCountFrequency (%) 
057993.5%
 
1406.5%
 
2021-11-29T18:41:59.610782image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

finalist_WC
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0
619 
ValueCountFrequency (%) 
0619100.0%
 
2021-11-29T18:41:59.638474image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Interactions

2021-11-29T18:40:21.801378image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:21.904494image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:22.000628image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:22.110378image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:22.231467image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:22.333475image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:22.443475image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:22.546475image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:22.653601image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:22.751415image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:22.844622image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:22.940910image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:23.048320image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:23.149450image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:23.315767image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:23.473357image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:23.630311image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:23.790019image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:23.932387image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:24.074876image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:24.211742image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:24.358910image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:24.505647image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:24.586290image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:24.666680image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:24.750554image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:24.869197image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:24.964382image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:25.049670image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:25.135582image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:25.232440image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:25.319288image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:25.403596image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:25.490852image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:25.578213image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:25.667544image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:25.755500image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:25.840536image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:25.924451image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:26.007512image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:26.084129image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:26.168216image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:28.749810image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:28.894317image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:29.026317image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:29.134315image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:29.227219image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:29.308140image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:29.391143image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:29.481671image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:29.567140image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:29.654213image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:29.740757image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:29.820371image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:29.911278image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:29.996361image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:30.078314image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:30.165320image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:30.261211image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:30.347717image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:30.431728image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:30.514724image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:30.599746image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:30.684378image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:30.769339image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:30.847817image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:30.929606image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:31.006498image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:31.088413image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:31.174637image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:31.260545image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:31.342659image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:31.426615image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:31.503472image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:31.613796image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:31.767414image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:32.029830image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:32.121259image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:32.218847image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:32.316853image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:32.411401image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:32.516350image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:32.614135image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:32.705557image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:32.803556image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:32.898867image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:32.998627image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:33.098392image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:33.196038image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:33.285861image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:33.381346image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:33.469537image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:33.559549image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:33.650923image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:33.735586image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:33.825531image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:33.910521image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:34.000850image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:34.086602image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:34.177189image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:34.259295image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:34.343563image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:34.433552image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:34.521421image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:34.606516image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:34.690610image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:34.777489image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:34.861025image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:34.947697image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:35.034547image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:35.119433image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:35.205680image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:35.293176image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:35.380465image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:35.467553image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:35.564686image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:35.650491image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:35.739511image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:36.000640image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:36.089550image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:36.177498image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:36.258551image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:36.344617image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:36.429381image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:36.519620image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:36.608196image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:36.697584image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:36.778660image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:36.862675image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:36.949581image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:37.057276image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:37.143099image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:37.226420image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:37.318592image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:37.405767image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:37.491342image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:37.576877image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:37.658950image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:37.765381image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:37.852881image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:37.944381image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:40:38.034575image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-29T18:42:01.542594image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-11-29T18:42:02.007210image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-11-29T18:41:47.533449image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:41:48.212704image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-11-29T18:41:48.528261image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Sample

First rows

Season_End_YearSquadCompPlayerNationPosAgeBornMP_PlayingStarts_PlayingMin_PlayingMins_Per_90_PlayingGlsAstG_minus_PKPKPKattCrdYCrdRGls_PerAst_PerG+A_PerG_minus_PK_PerG+A_minus_PK_PerUrlxG_ExpectednpxG_ExpectedxA_ExpectednpxG+xA_ExpectedxG_PerxA_PerxG+xA_PernpxG_PernpxG+xA_PerBO_JKNomPaysClub%UCL_JKwon_UCLSquad_JKNation_JKwinner_Bundesligawinner_C3finalist_C3winner_UCLfinalist_UCLwinner_Club WCfinalist_Club WCwinner_Copa Americafinalist_Copa Americawinner_Eurofinalist_Eurowinner_Ligawinner_Ligue 1winner_PLwinner_Serie Awinner_WCfinalist_WC
01996BlackburnPremier LeagueAlan ShearerENGFW24.01970.035.035.03150.035.031.07.028.03.03.04.00.00.890.21.090.801.0https://fbref.com/en/players/438b3a51/Alan-ShearerNaNNaNNaNNaNNaNNaNNaNNaNNaN1996Alan ShearerAlan ShearerAngleterreBlackburn Rovers / Newcastle United0.139869281045751641996Blackburn01996Blackburn1996ENG00000000000000000
11996JuventusSerie AAlessandro Del PieroITAFW,MF20.01974.029.025.02111.023.56.0NaN6.00.0NaN3.00.00.26NaNNaN0.26NaNhttps://fbref.com/en/players/80bc263c/Alessandro-Del-PieroNaNNaNNaNNaNNaNNaNNaNNaNNaN1996Alessandro Del PieroAlessandro Del PieroItalieJuventus0.090196078431372551996Juventus11996Juventus1996ITA00000000000000000
21996Bayern MunichBundesligaJürgen KlinsmannGERFW31.01964.032.032.02722.030.216.0NaN13.03.0NaN0.00.00.53NaNNaN0.43NaNhttps://fbref.com/en/players/7f97bf11/Jurgen-KlinsmannNaNNaNNaNNaNNaNNaNNaNNaNNaN1996Jürgen KlinsmannJürgen KlinsmannAllemagneBayern Munich0.07843137254901961996Bayern Munich01996Bayern Munich1996GER00000000010000000
31996DortmundBundesligaMatthias SammerGERDF,MF27.01967.022.022.01965.021.83.0NaN3.00.0NaN10.00.00.14NaNNaN0.14NaNhttps://fbref.com/en/players/d8ae851f/Matthias-SammerNaNNaNNaNNaNNaNNaNNaNNaNNaN1996Matthias SammerMatthias SammerAllemagneBorussia Dortmund0.188235294117647061996Dortmund01996Dortmund1996GER00000000010000000
41996BlackburnPremier LeagueAlan ShearerENGFW24.01970.035.035.03150.035.031.07.028.03.03.04.00.00.890.21.090.801.0https://fbref.com/en/players/438b3a51/Alan-ShearerNaNNaNNaNNaNNaNNaNNaNNaNNaN1996Alan ShearerAlan ShearerAngleterreBlackburn Rovers / Newcastle United0.139869281045751641996Blackburn01996Blackburn1996ENG00000000000000000
51996JuventusSerie AAlessandro Del PieroITAFW,MF20.01974.029.025.02111.023.56.0NaN6.00.0NaN3.00.00.26NaNNaN0.26NaNhttps://fbref.com/en/players/80bc263c/Alessandro-Del-PieroNaNNaNNaNNaNNaNNaNNaNNaNNaN1996Alessandro Del PieroAlessandro Del PieroItalieJuventus0.090196078431372551996Juventus11996Juventus1996ITA00000000000000000
61996Bayern MunichBundesligaJürgen KlinsmannGERFW31.01964.032.032.02722.030.216.0NaN13.03.0NaN0.00.00.53NaNNaN0.43NaNhttps://fbref.com/en/players/7f97bf11/Jurgen-KlinsmannNaNNaNNaNNaNNaNNaNNaNNaNNaN1996Jürgen KlinsmannJürgen KlinsmannAllemagneBayern Munich0.07843137254901961996Bayern Munich01996Bayern Munich1996GER00000000010000000
71996SevillaLa LigaDavor ŠukerCROFW27.01968.032.031.02629.029.216.0NaN14.02.0NaN11.03.00.55NaNNaN0.48NaNhttps://fbref.com/en/players/f3580cae/Davor-SukerNaNNaNNaNNaNNaNNaNNaNNaNNaN1996Davor ŠukerDavor ŠukerCroatieFC Séville / Real Madrid0.049673202614379081996Sevilla01996Sevilla1996CRO00000000000000000
81996MilanSerie AMarcel DesaillyFRADF,MF26.01968.032.032.02872.031.92.0NaN2.00.0NaN5.00.00.06NaNNaN0.06NaNhttps://fbref.com/en/players/9dfb97aa/Marcel-DesaillyNaNNaNNaNNaNNaNNaNNaNNaNNaN1996Marcel DesaillyMarcel DesaillyFranceMilan AC0.028758169934640521996Milan01996Milan1996FRA00000000000000000
91996Paris S-GDivision 1Youri DjorkaeffFRAFW,MF27.01968.035.033.02898.032.213.0NaN12.01.0NaN5.00.00.40NaNNaN0.37NaNhttps://fbref.com/en/players/e9f1bedd/Youri-DjorkaeffNaNNaNNaNNaNNaNNaNNaNNaNNaN1996Youri DjorkaeffYouri DjorkaeffFrancePSG /Inter Milan0.0261437908496732031996Paris S-G01996Paris S-G1996FRA00000000000000000

Last rows

Season_End_YearSquadCompPlayerNationPosAgeBornMP_PlayingStarts_PlayingMin_PlayingMins_Per_90_PlayingGlsAstG_minus_PKPKPKattCrdYCrdRGls_PerAst_PerG+A_PerG_minus_PK_PerG+A_minus_PK_PerUrlxG_ExpectednpxG_ExpectedxA_ExpectednpxG+xA_ExpectedxG_PerxA_PerxG+xA_PernpxG_PernpxG+xA_PerBO_JKNomPaysClub%UCL_JKwon_UCLSquad_JKNation_JKwinner_Bundesligawinner_C3finalist_C3winner_UCLfinalist_UCLwinner_Club WCfinalist_Club WCwinner_Copa Americafinalist_Copa Americawinner_Eurofinalist_Eurowinner_Ligawinner_Ligue 1winner_PLwinner_Serie Awinner_WCfinalist_WC
6092019Atlético MadridLa LigaAntoine GriezmannFRAFW27.01991.037.037.03198.035.515.08.012.03.03.05.00.00.420.230.650.340.56https://fbref.com/en/players/df69b544/Antoine-Griezmann13.711.46.818.20.390.190.580.320.512019Antoine GriezmannAntoine GriezmannFranceAtlético de Madrid FC Barcelone0.00319602272727272752019Atlético Madrid02019Atlético Madrid2019FRA00000000000000000
6102019LiverpoolPremier LeagueTrent Alexander-ArnoldENGDF19.01998.029.027.02462.027.41.012.01.00.00.03.00.00.040.440.480.040.48https://fbref.com/en/players/cd1acf9d/Trent-Alexander-Arnold1.41.46.37.70.050.230.280.050.282019Trent Alexander-ArnoldTrent Alexander-ArnoldAngleterreLiverpool FC0.0028409090909090912019Liverpool12019Liverpool2019ENG00000000000000000
6112019ArsenalPremier LeaguePierre-Emerick AubameyangGABFW29.01989.036.030.02726.030.322.05.018.04.05.00.00.00.730.170.890.590.76https://fbref.com/en/players/d5dd5f1f/Pierre-Emerick-Aubameyang20.116.24.220.40.670.140.800.530.672019Pierre-Emerick AubameyangPierre-Emerick AubameyangGabonArsenal FC0.00177556818181818182019Arsenal02019Arsenal2019GAB00000000000000000
6122019TottenhamPremier LeagueSon Heung-minKORFW,MF26.01992.031.023.02039.022.712.06.012.00.00.02.01.00.530.260.790.530.79https://fbref.com/en/players/92e7e919/Son-Heung-min7.77.73.311.10.340.150.490.340.492019Son Heung-minSon Heung-minCorée du SudTottenham Hotspur0.00142045454545454552019Tottenham02019Tottenham2019KOR00000000000000000
6132019TottenhamPremier LeagueHugo LlorisFRAGK31.01986.033.033.02970.033.00.00.00.00.00.00.00.00.000.000.000.000.00https://fbref.com/en/players/8f62b6ee/Hugo-Lloris0.00.00.00.00.000.000.000.000.002019Hugo LlorisHugo LlorisFranceTottenham Hotspur0.0010653409090909092019Tottenham02019Tottenham2019FRA00000000000000000
6142019NapoliSerie AKalidou KoulibalySENDF27.01991.035.035.03132.034.82.02.02.00.00.010.01.00.060.060.110.060.11https://fbref.com/en/players/da974c7b/Kalidou-Koulibaly3.23.20.23.50.090.010.100.090.102019Kalidou KoulibalyKalidou KoulibalySénégalSSC Naples0.00071022727272727272019Napoli02019Napoli2019SEN00000000000000000
6152019BarcelonaLa LigaMarc-André ter StegenGERGK26.01992.035.035.03150.035.00.00.00.00.00.00.00.00.000.000.000.000.00https://fbref.com/en/players/6f51e382/Marc-Andre-ter-Stegen0.00.00.00.00.000.000.000.000.002019Marc-André ter StegenMarc-André ter StegenAllemagneFC Barcelone0.00071022727272727272019Barcelona02019Barcelona2019GER00000000000000000
6162019Real MadridLa LigaKarim BenzemaFRAFW30.01987.036.035.02953.032.821.06.018.03.03.01.00.00.640.180.820.550.73https://fbref.com/en/players/70d74ece/Karim-Benzema15.813.55.619.10.480.170.650.410.582019Karim BenzemaKarim BenzemaFranceReal Madrid0.00035511363636363642019Real Madrid02019Real Madrid2019FRA00000000000000000
6172019LiverpoolPremier LeagueGeorginio WijnaldumNEDMF27.01990.035.032.02725.030.33.00.03.00.00.03.00.00.100.000.100.100.10https://fbref.com/en/players/eb58eef0/Georginio-Wijnaldum2.82.81.24.00.090.040.130.090.132019Georginio WijnaldumGeorginio WijnaldumPays-BasLiverpool FC0.00035511363636363642019Liverpool12019Liverpool2019NED00000000000000000
6182019Paris S-GLigue 1MarquinhosBRADF,MF24.01994.030.030.02693.029.93.02.03.00.00.02.01.00.100.070.170.100.17https://fbref.com/en/players/d5f2f82b/Marquinhos1.21.21.93.10.040.060.110.040.112019MarquinhosMarquinhosBrésilParis Saint-Germain0.02019Paris S-G02019Paris S-G2019BRA00000001000000000